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263 results (View BibTeX file of all listed publications)

2017

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Optical Flow Estimation using a Spatial Pyramid Network

Ranjan, A., Black, M.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions; these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small (< 1 pixel), a convolutional approach applied to pairs of warped images is appropriate. Third, unlike FlowNet, the learned convolution filters appear similar to classical spatio-temporal filters, giving insight into the method and how to improve it. Our results are more accurate than FlowNet on most standard benchmarks, suggesting a new direction of combining classical flow methods with deep learning.

pdf SupMat project/code [BibTex]

2017

Ranjan, A., Black, M. Optical Flow Estimation using a Spatial Pyramid Network In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pdf SupMat project/code [BibTex]

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Multiple People Tracking by Lifted Multicut and Person Re-identification

Tang, S., Andriluka, M., Andres, B., Schiele, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3701-3710, IEEE Computer Society, Washington, DC, USA, July 2017 (inproceedings)

DOI Project Page [BibTex]

Tang, S., Andriluka, M., Andres, B., Schiele, B. Multiple People Tracking by Lifted Multicut and Person Re-identification In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3701-3710, IEEE Computer Society, Washington, DC, USA, July 2017 (inproceedings)

DOI Project Page [BibTex]

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Video Propagation Networks

Jampani, V., Gadde, R., Gehler, P. V.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pdf supplementary arXiv project page code Project Page [BibTex]

Jampani, V., Gadde, R., Gehler, P. V. Video Propagation Networks In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pdf supplementary arXiv project page code Project Page [BibTex]

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Generating Descriptions with Grounded and Co-Referenced People

Rohrbach, A., Rohrbach, M., Tang, S., Oh, S. J., Schiele, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 4196-4206, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

PDF DOI Project Page [BibTex]

Rohrbach, A., Rohrbach, M., Tang, S., Oh, S. J., Schiele, B. Generating Descriptions with Grounded and Co-Referenced People In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 4196-4206, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

PDF DOI Project Page [BibTex]

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Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels

Ulusoy, A. O., Black, M. J., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
Dense 3D reconstruction from RGB images is a highly ill-posed problem due to occlusions, textureless or reflective surfaces, as well as other challenges. We propose object-level shape priors to address these ambiguities. Towards this goal, we formulate a probabilistic model that integrates multi-view image evidence with 3D shape information from multiple objects. Inference in this model yields a dense 3D reconstruction of the scene as well as the existence and precise 3D pose of the objects in it. Our approach is able to recover fine details not captured in the input shapes while defaulting to the input models in occluded regions where image evidence is weak. Due to its probabilistic nature, the approach is able to cope with the approximate geometry of the 3D models as well as input shapes that are not present in the scene. We evaluate the approach quantitatively on several challenging indoor and outdoor datasets.

YouTube pdf suppmat Project Page [BibTex]

Ulusoy, A. O., Black, M. J., Geiger, A. Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

YouTube pdf suppmat Project Page [BibTex]

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Deep representation learning for human motion prediction and classification

Bütepage, J., Black, M., Kragic, D., Kjellström, H.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

arXiv Project Page [BibTex]

Bütepage, J., Black, M., Kragic, D., Kjellström, H. Deep representation learning for human motion prediction and classification In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

arXiv Project Page [BibTex]

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Unite the People: Closing the Loop Between 3D and 2D Human Representations

Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., Gehler, P. V.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits “in-the-wild”. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-of-the art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.

arXiv project/code/data Project Page [BibTex]

Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., Gehler, P. V. Unite the People: Closing the Loop Between 3D and 2D Human Representations In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

arXiv project/code/data Project Page [BibTex]

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System and method for simulating realistic clothing

Black, M. J., Guan, P.

June 2017, U.S.~Patent 9,679,409 B2 (misc)

Abstract
Systems, methods, and computer-readable storage media for simulating realistic clothing. The system generates a clothing deformation model for a clothing type, wherein the clothing deformation model factors a change of clothing shape due to rigid limb rotation, pose-independent body shape, and pose-dependent deformations. Next, the system generates a custom-shaped garment for a given body by mapping, via the clothing deformation model, body shape parameters to clothing shape parameters. The system then automatically dresses the given body with the custom- shaped garment.

Google Patents pdf [BibTex]

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Human Shape Estimation using Statistical Body Models

Loper, M. M.

University of Tübingen, May 2017 (thesis)

Abstract
Human body estimation methods transform real-world observations into predictions about human body state. These estimation methods benefit a variety of health, entertainment, clothing, and ergonomics applications. State may include pose, overall body shape, and appearance. Body state estimation is underconstrained by observations; ambiguity presents itself both in the form of missing data within observations, and also in the form of unknown correspondences between observations. We address this challenge with the use of a statistical body model: a data-driven virtual human. This helps resolve ambiguity in two ways. First, it fills in missing data, meaning that incomplete observations still result in complete shape estimates. Second, the model provides a statistically-motivated penalty for unlikely states, which enables more plausible body shape estimates. Body state inference requires more than a body model; we therefore build obser- vation models whose output is compared with real observations. In this thesis, body state is estimated from three types of observations: 3D motion capture markers, depth and color images, and high-resolution 3D scans. In each case, a forward process is proposed which simulates observations. By comparing observations to the results of the forward process, state can be adjusted to minimize the difference between simulated and observed data. We use gradient-based methods because they are critical to the precise estimation of state with a large number of parameters. The contributions of this work include three parts. First, we propose a method for the estimation of body shape, nonrigid deformation, and pose from 3D markers. Second, we present a concise approach to differentiating through the rendering process, with application to body shape estimation. And finally, we present a statistical body model trained from human body scans, with state-of-the-art fidelity, good runtime performance, and compatibility with existing animation packages.

Official Version [BibTex]

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Early Stopping Without a Validation Set

Mahsereci, M., Balles, L., Lassner, C., Hennig, P.

arXiv preprint arXiv:1703.09580, 2017 (article)

Abstract
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. In this paper we propose a novel early stopping criterion which is based on fast-to-compute, local statistics of the computed gradients and entirely removes the need for a held-out validation set. Our experiments show that this is a viable approach in the setting of least-squares and logistic regression as well as neural networks.

link (url) Project Page Project Page [BibTex]

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Appealing Avatars from 3D Body Scans: Perceptual Effects of Stylization

Fleming, R., Mohler, B. J., Romero, J., Black, M. J., Breidt, M.

In Computer Vision, Imaging and Computer Graphics Theory and Applications: 11th International Joint Conference, VISIGRAPP 2016, Rome, Italy, February 27 – 29, 2016, Revised Selected Papers, pages: 175-196, Springer International Publishing, 2017 (inbook)

Abstract
Using styles derived from existing popular character designs, we present a novel automatic stylization technique for body shape and colour information based on a statistical 3D model of human bodies. We investigate whether such stylized body shapes result in increased perceived appeal with two different experiments: One focuses on body shape alone, the other investigates the additional role of surface colour and lighting. Our results consistently show that the most appealing avatar is a partially stylized one. Importantly, avatars with high stylization or no stylization at all were rated to have the least appeal. The inclusion of colour information and improvements to render quality had no significant effect on the overall perceived appeal of the avatars, and we observe that the body shape primarily drives the change in appeal ratings. For body scans with colour information, we found that a partially stylized avatar was perceived as most appealing.

publisher site pdf DOI [BibTex]

Fleming, R., Mohler, B. J., Romero, J., Black, M. J., Breidt, M. Appealing Avatars from 3D Body Scans: Perceptual Effects of Stylization In Computer Vision, Imaging and Computer Graphics Theory and Applications: 11th International Joint Conference, VISIGRAPP 2016, Rome, Italy, February 27 – 29, 2016, Revised Selected Papers, pages: 175-196, Springer International Publishing, 2017 (inbook)

publisher site pdf DOI [BibTex]

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Learning to Filter Object Detections

Prokudin, S., Kappler, D., Nowozin, S., Gehler, P.

In Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland, September 12–15, 2017, Proceedings, pages: 52-62, Springer International Publishing, Cham, 2017 (inbook)

Abstract
Most object detection systems consist of three stages. First, a set of individual hypotheses for object locations is generated using a proposal generating algorithm. Second, a classifier scores every generated hypothesis independently to obtain a multi-class prediction. Finally, all scored hypotheses are filtered via a non-differentiable and decoupled non-maximum suppression (NMS) post-processing step. In this paper, we propose a filtering network (FNet), a method which replaces NMS with a differentiable neural network that allows joint reasoning and re-scoring of the generated set of hypotheses per image. This formulation enables end-to-end training of the full object detection pipeline. First, we demonstrate that FNet, a feed-forward network architecture, is able to mimic NMS decisions, despite the sequential nature of NMS. We further analyze NMS failures and propose a loss formulation that is better aligned with the mean average precision (mAP) evaluation metric. We evaluate FNet on several standard detection datasets. Results surpass standard NMS on highly occluded settings of a synthetic overlapping MNIST dataset and show competitive behavior on PascalVOC2007 and KITTI detection benchmarks.

Paper link (url) DOI Project Page [BibTex]

Prokudin, S., Kappler, D., Nowozin, S., Gehler, P. Learning to Filter Object Detections In Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland, September 12–15, 2017, Proceedings, pages: 52-62, Springer International Publishing, Cham, 2017 (inbook)

Paper link (url) DOI Project Page [BibTex]

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Data-Driven Physics for Human Soft Tissue Animation

Kim, M., Pons-Moll, G., Pujades, S., Bang, S., Kim, J., Black, M. J., Lee, S.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):54:1-54:12, 2017 (article)

Abstract
Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.

video paper link (url) Project Page [BibTex]

Kim, M., Pons-Moll, G., Pujades, S., Bang, S., Kim, J., Black, M. J., Lee, S. Data-Driven Physics for Human Soft Tissue Animation ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):54:1-54:12, 2017 (article)

video paper link (url) Project Page [BibTex]

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Learning Inference Models for Computer Vision

Jampani, V.

MPI for Intelligent Systems and University of Tübingen, 2017 (phdthesis)

Abstract
Computer vision can be understood as the ability to perform 'inference' on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call `Bilateral Neural Networks'. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. Experiments demonstrate the potential of the proposed bilateral networks on a wide range of vision tasks and datasets. In summary, we propose learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks. In generative vision models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way for incorporating prior knowledge into CNNs.

pdf [BibTex]

Jampani, V. Learning Inference Models for Computer Vision MPI for Intelligent Systems and University of Tübingen, 2017 (phdthesis)

pdf [BibTex]

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Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

(Best Paper, Eurographics 2017)

Marcard, T. V., Rosenhahn, B., Black, M., Pons-Moll, G.

Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics), pages: 349-360 , 2017 (article)

Abstract
We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall

video pdf Project Page [BibTex]

Marcard, T. V., Rosenhahn, B., Black, M., Pons-Moll, G. Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics), pages: 349-360 , 2017 (article)

video pdf Project Page [BibTex]

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Efficient 2D and 3D Facade Segmentation using Auto-Context

Gadde, R., Jampani, V., Marlet, R., Gehler, P.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (article)

Abstract
This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference.

arXiv Project Page [BibTex]

Gadde, R., Jampani, V., Marlet, R., Gehler, P. Efficient 2D and 3D Facade Segmentation using Auto-Context IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (article)

arXiv Project Page [BibTex]

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ClothCap: Seamless 4D Clothing Capture and Retargeting

Pons-Moll, G., Pujades, S., Hu, S., Black, M.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):73:1-73:15, ACM, New York, NY, USA, 2017, Two first authors contributed equally (article)

Abstract
Designing and simulating realistic clothing is challenging and, while several methods have addressed the capture of clothing from 3D scans, previous methods have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the naked body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. The model allows us to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes. ClothCap provides a step towards virtual try-on with a technology for capturing, modeling, and analyzing clothing in motion.

video project_page paper link (url) DOI Project Page Project Page [BibTex]

Pons-Moll, G., Pujades, S., Hu, S., Black, M. ClothCap: Seamless 4D Clothing Capture and Retargeting ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):73:1-73:15, ACM, New York, NY, USA, 2017, Two first authors contributed equally (article)

video project_page paper link (url) DOI Project Page Project Page [BibTex]

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Towards Accurate Marker-less Human Shape and Pose Estimation over Time

Huang, Y., Bogo, F., Lassner, C., Kanazawa, A., Gehler, P. V., Romero, J., Akhter, I., Black, M. J.

In International Conference on 3D Vision (3DV), pages: 421-430, 2017 (inproceedings)

Abstract
Existing markerless motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, limiting their application scenarios. Here we present a fully automatic method that, given multiview videos, estimates 3D human pose and body shape. We take the recently proposed SMPLify method [12] as the base method and extend it in several ways. First we fit a 3D human body model to 2D features detected in multi-view images. Second, we use a CNN method to segment the person in each image and fit the 3D body model to the contours, further improving accuracy. Third we utilize a generic and robust DCT temporal prior to handle the left and right side swapping issue sometimes introduced by the 2D pose estimator. Validation on standard benchmarks shows our results are comparable to the state of the art and also provide a realistic 3D shape avatar. We also demonstrate accurate results on HumanEva and on challenging monocular sequences of dancing from YouTube.

Code pdf DOI Project Page [BibTex]

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Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots

Nestmeyer, T., Robuffo Giordano, P., Bülthoff, H. H., Franchi, A.

In pages: 989-1011, Autonomous Robots, 2017 (incollection)

[BibTex]

Nestmeyer, T., Robuffo Giordano, P., Bülthoff, H. H., Franchi, A. Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots In pages: 989-1011, Autonomous Robots, 2017 (incollection)

[BibTex]

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Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Tzionas, D.

University of Bonn, 2017 (phdthesis)

Abstract
Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object's shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow.

Thesis link (url) Project Page [BibTex]

2015

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Exploiting Object Similarity in 3D Reconstruction

Zhou, C., Güney, F., Wang, Y., Geiger, A.

In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavor. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by locating objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. We evaluate our approach with respect to LIDAR ground truth on a novel challenging suburban dataset and show its advantages over the state-of-the-art.

pdf suppmat [BibTex]

2015

Zhou, C., Güney, F., Wang, Y., Geiger, A. Exploiting Object Similarity in 3D Reconstruction In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

pdf suppmat [BibTex]

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FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

Lenz, P., Geiger, A., Urtasun, R.

In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrary length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

pdf suppmat video project [BibTex]

Lenz, P., Geiger, A., Urtasun, R. FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

pdf suppmat video project [BibTex]

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Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

preprint pdf from publisher supplemental Project Page [BibTex]

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Intrinsic Depth: Improving Depth Transfer with Intrinsic Images

Kong, N., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3514-3522, December 2015 (inproceedings)

Abstract
We formulate the estimation of dense depth maps from video sequences as a problem of intrinsic image estimation. Our approach synergistically integrates the estimation of multiple intrinsic images including depth, albedo, shading, optical flow, and surface contours. We build upon an example-based framework for depth estimation that uses label transfer from a database of RGB and depth pairs. We combine this with a method that extracts consistent albedo and shading from video. In contrast to raw RGB values, albedo and shading provide a richer, more physical, foundation for depth transfer. Additionally we train a new contour detector to predict surface boundaries from albedo, shading, and pixel values and use this to improve the estimation of depth boundaries. We also integrate sparse structure from motion with our method to improve the metric accuracy of the estimated depth maps. We evaluate our Intrinsic Depth method quantitatively by estimating depth from videos in the NYU RGB-D and SUN3D datasets. We find that combining the estimation of multiple intrinsic images improves depth estimation relative to the baseline method.

pdf suppmat YouTube official video poster Project Page Project Page [BibTex]

Kong, N., Black, M. J. Intrinsic Depth: Improving Depth Transfer with Intrinsic Images In IEEE International Conference on Computer Vision (ICCV), pages: 3514-3522, December 2015 (inproceedings)

pdf suppmat YouTube official video poster Project Page Project Page [BibTex]

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Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences

Bogo, F., Black, M. J., Loper, M., Romero, J.

In International Conference on Computer Vision (ICCV), pages: 2300-2308, December 2015 (inproceedings)

Abstract
We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.

Video pdf Project Page Project Page [BibTex]

Bogo, F., Black, M. J., Loper, M., Romero, J. Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences In International Conference on Computer Vision (ICCV), pages: 2300-2308, December 2015 (inproceedings)

Video pdf Project Page Project Page [BibTex]

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3D Object Reconstruction from Hand-Object Interactions

Tzionas, D., Gall, J.

In International Conference on Computer Vision (ICCV), pages: 729-737, December 2015 (inproceedings)

Abstract
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.

pdf Project's Website Video Spotlight Extended Abstract YouTube DOI Project Page [BibTex]

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SMPL: A Skinned Multi-Person Linear Model

Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M. J.

ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1-248:16, ACM, New York, NY, October 2015 (article)

Abstract
We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

pdf video code/model errata DOI Project Page Project Page [BibTex]

Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M. J. SMPL: A Skinned Multi-Person Linear Model ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1-248:16, ACM, New York, NY, October 2015 (article)

pdf video code/model errata DOI Project Page Project Page [BibTex]

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The fertilized forests Decision Forest Library

Lassner, C., Lienhart, R.

In ACM Transactions on Multimedia (ACMMM) Open-source Software Competition, October 2015 (inproceedings)

Abstract
Since the introduction of Random Forests in the 80's they have been a frequently used statistical tool for a variety of machine learning tasks. Many different training algorithms and model adaptions demonstrate the versatility of the forests. This variety resulted in a fragmentation of research and code, since each adaption requires its own algorithms and representations. In 2011, Criminisi and Shotton developed a unifying Decision Forest model for many tasks. By identifying the reusable parts and specifying clear interfaces, we extend this approach to an object oriented representation and implementation. This has the great advantage that research on specific parts of the Decision Forest model can be done `locally' by reusing well-tested and high-performance components. Our fertilized forests library is open source and easy to extend. It provides components allowing for parallelization up to node optimization level to exploit modern many core architectures. Additionally, the library provides consistent and easy-to-maintain interfaces to C++, Python and Matlab and offers cross-platform and cross-interface persistence.

website and code pdf [BibTex]

Lassner, C., Lienhart, R. The fertilized forests Decision Forest Library In ACM Transactions on Multimedia (ACMMM) Open-source Software Competition, October 2015 (inproceedings)

website and code pdf [BibTex]

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Proceedings of the 37th German Conference on Pattern Recognition

Gall, J., Gehler, P., Leibe, B.

Springer, German Conference on Pattern Recognition, October 2015 (proceedings)

GCPR conference website [BibTex]

Gall, J., Gehler, P., Leibe, B. Proceedings of the 37th German Conference on Pattern Recognition Springer, German Conference on Pattern Recognition, October 2015 (proceedings)

GCPR conference website [BibTex]

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Towards Probabilistic Volumetric Reconstruction using Ray Potentials

(Best Paper Award)

Ulusoy, A. O., Geiger, A., Black, M. J.

In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

Abstract
This paper presents a novel probabilistic foundation for volumetric 3-d reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty.

code YouTube pdf suppmat DOI Project Page [BibTex]

Ulusoy, A. O., Geiger, A., Black, M. J. Towards Probabilistic Volumetric Reconstruction using Ray Potentials In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

code YouTube pdf suppmat DOI Project Page [BibTex]

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Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception

Ahmad, A., Bülthoff, H.

7th European Conference on Mobile Robots, pages: 1-8, September 2015 (conference)

Abstract
In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with i) an extended Kalman filter-based online-estimator and ii) an offline-estimator based on full-trajectory nonlinear least squares.

DOI [BibTex]

Ahmad, A., Bülthoff, H. Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception 7th European Conference on Mobile Robots, pages: 1-8, September 2015 (conference)

DOI [BibTex]

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Perception of Strength and Power of Realistic Male Characters

Wellerdiek, A. C., Breidt, M., Geuss, M. N., Streuber, S., Kloos, U., Black, M. J., Mohler, B. J.

In Proc. ACM SIGGRAPH Symposium on Applied Perception, SAP’15, pages: 7-14, ACM, New York, NY, September 2015 (inproceedings)

Abstract
We investigated the influence of body shape and pose on the perception of physical strength and social power for male virtual characters. In the first experiment, participants judged the physical strength of varying body shapes, derived from a statistical 3D body model. Based on these ratings, we determined three body shapes (weak, average, and strong) and animated them with a set of power poses for the second experiment. Participants rated how strong or powerful they perceived virtual characters of varying body shapes that were displayed in different poses. Our results show that perception of physical strength was mainly driven by the shape of the body. However, the social attribute of power was influenced by an interaction between pose and shape. Specifically, the effect of pose on power ratings was greater for weak body shapes. These results demonstrate that a character with a weak shape can be perceived as more powerful when in a high-power pose.

PDF DOI Project Page [BibTex]

Wellerdiek, A. C., Breidt, M., Geuss, M. N., Streuber, S., Kloos, U., Black, M. J., Mohler, B. J. Perception of Strength and Power of Realistic Male Characters In Proc. ACM SIGGRAPH Symposium on Applied Perception, SAP’15, pages: 7-14, ACM, New York, NY, September 2015 (inproceedings)

PDF DOI Project Page [BibTex]

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Human Pose as Context for Object Detection

Srikantha, A., Gall, J.

British Machine Vision Conference, September 2015 (conference)

Abstract
Detecting small objects in images is a challenging problem particularly when they are often occluded by hands or other body parts. Recently, joint modelling of human pose and objects has been proposed to improve both pose estimation as well as object detection. These approaches, however, focus on explicit interaction with an object and lack the flexibility to combine both modalities when interaction is not obvious. We therefore propose to use human pose as an additional context information for object detection. To this end, we represent an object category by a tree model and train regression forests that localize parts of an object for each modality separately. Predictions of the two modalities are then combined to detect the bounding box of the object. We evaluate our approach on three challenging datasets which vary in the amount of object interactions and the quality of automatically extracted human poses.

pdf abstract Project Page [BibTex]

Srikantha, A., Gall, J. Human Pose as Context for Object Detection British Machine Vision Conference, September 2015 (conference)

pdf abstract Project Page [BibTex]

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Active Learning for Efficient Sampling of Control Models of Collectives

Schiendorfer, A., Lassner, C., Anders, G., Reif, W., Lienhart, R.

In International Conference on Self-adaptive and Self-organizing Systems (SASO), September 2015 (inproceedings)

Abstract
Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be modified autonomously by, e.g., coalition formation and reconfiguration, adequate decisions on higher levels require a faithful abstracted model of a collective of agents. An illustrative example is found in calculating schedules for a set of power plants organized in a hierarchy of Autonomous Virtual Power Plants. Functional dependencies over the combinatorial domain, such as the joint costs or rates of change of power production, are approximated by repeatedly sampling input-output pairs and substituting the actual functions by piecewise linear functions. However, if the sampled data points are weakly informative, the resulting abstracted high-level optimization introduces severe errors. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Building on prior work, we propose to apply techniques from active learning to maximize the information gained by each additional point. Our results show that significantly better allocations in terms of cost-efficiency (up to 33.7 % reduction in costs in our case study) can be found with fewer but carefully selected sampling points using Decision Forests.

code (hosted on github) [BibTex]

Schiendorfer, A., Lassner, C., Anders, G., Reif, W., Lienhart, R. Active Learning for Efficient Sampling of Control Models of Collectives In International Conference on Self-adaptive and Self-organizing Systems (SASO), September 2015 (inproceedings)

code (hosted on github) [BibTex]

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Dyna: A Model of Dynamic Human Shape in Motion

Pons-Moll, G., Romero, J., Mahmood, N., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 34(4):120:1-120:14, ACM, August 2015 (article)

Abstract
To look human, digital full-body avatars need to have soft tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D capture system and a method that accurately registers a template mesh to sequences of 3D scans. Using over 40,000 scans of ten subjects, we learn how soft tissue motion causes mesh triangles to deform relative to a base 3D body model. Our Dyna model uses a low-dimensional linear subspace to approximate soft-tissue deformation and relates the subspace coefficients to the changing pose of the body. Dyna uses a second-order auto-regressive model that predicts soft-tissue deformations based on previous deformations, the velocity and acceleration of the body, and the angular velocities and accelerations of the limbs. Dyna also models how deformations vary with a person’s body mass index (BMI), producing different deformations for people with different shapes. Dyna realistically represents the dynamics of soft tissue for previously unseen subjects and motions. We provide tools for animators to modify the deformations and apply them to new stylized characters.

pdf preprint video data DOI Project Page Project Page [BibTex]

Pons-Moll, G., Romero, J., Mahmood, N., Black, M. J. Dyna: A Model of Dynamic Human Shape in Motion ACM Transactions on Graphics, (Proc. SIGGRAPH), 34(4):120:1-120:14, ACM, August 2015 (article)

pdf preprint video data DOI Project Page Project Page [BibTex]

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The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

Jampani, V., Nowozin, S., Loper, M., Gehler, P. V.

In Computer Vision and Image Understanding, Special Issue on Generative Models in Computer Vision and Medical Imaging, 136, pages: 32-44, Elsevier, July 2015 (inproceedings)

Abstract
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Bayesian posterior inference could then, in principle, explain the observation. While intuitively appealing, generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference. As a result the community has favored efficient discriminative approaches. We still believe in the usefulness of generative models in computer vision, but argue that we need to leverage existing discriminative or even heuristic computer vision methods. We implement this idea in a principled way in our informed sampler and in careful experiments demonstrate it on challenging models which contain renderer programs as their components. The informed sampler, using simple discriminative proposals based on existing computer vision technology achieves dramatic improvements in inference. Our approach enables a new richness in generative models that was out of reach with existing inference technology.

arXiv-preprint pdf DOI Project Page [BibTex]

Jampani, V., Nowozin, S., Loper, M., Gehler, P. V. The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models In Computer Vision and Image Understanding, Special Issue on Generative Models in Computer Vision and Medical Imaging, 136, pages: 32-44, Elsevier, July 2015 (inproceedings)

arXiv-preprint pdf DOI Project Page [BibTex]

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Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex

Vargas-Irwin, C. E., Franquemont, L., Black, M. J., Donoghue, J. P.

Journal of Neuroscience, 35(30):10888-10897, July 2015 (article)

Abstract
Neural activity in ventral premotor cortex (PMv) has been associated with the process of matching perceived objects with the motor commands needed to grasp them. It remains unclear how PMv networks can flexibly link percepts of objects affording multiple grasp options into a final desired hand action. Here, we use a relational encoding approach to track the functional state of PMv neuronal ensembles in macaque monkeys through the process of passive viewing, grip planning, and grasping movement execution. We used objects affording multiple possible grip strategies. The task included separate instructed delay periods for object presentation and grip instruction. This approach allowed us to distinguish responses elicited by the visual presentation of the objects from those associated with selecting a given motor plan for grasping. We show that PMv continuously incorporates information related to object shape and grip strategy as it becomes available, revealing a transition from a set of ensemble states initially most closely related to objects, to a new set of ensemble patterns reflecting unique object-grip combinations. These results suggest that PMv dynamically combines percepts, gradually navigating toward activity patterns associated with specific volitional actions, rather than directly mapping perceptual object properties onto categorical grip representations. Our results support the idea that PMv is part of a network that dynamically computes motor plans from perceptual information. Significance Statement: The present work demonstrates that the activity of groups of neurons in primate ventral premotor cortex reflects information related to visually presented objects, as well as the motor strategy used to grasp them, linking individual objects to multiple possible grips. PMv could provide useful control signals for neuroprosthetic assistive devices designed to interact with objects in a flexible way.

publisher link DOI Project Page [BibTex]

Vargas-Irwin, C. E., Franquemont, L., Black, M. J., Donoghue, J. P. Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex Journal of Neuroscience, 35(30):10888-10897, July 2015 (article)

publisher link DOI Project Page [BibTex]

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Subgraph decomposition for multi-target tracking

Tang, S., Andres, B., Andriluka, M., Schiele, B.

In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 5033-5041, IEEE, June 2015 (inproceedings)

PDF Proof-of-Lemma-1 DOI [BibTex]

Tang, S., Andres, B., Andriluka, M., Schiele, B. Subgraph decomposition for multi-target tracking In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 5033-5041, IEEE, June 2015 (inproceedings)

PDF Proof-of-Lemma-1 DOI [BibTex]

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The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose

Zuffi, S., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 3537-3546, June 2015 (inproceedings)

Abstract
We propose a new 3D model of the human body that is both realistic and part-based. The body is represented by a graphical model in which nodes of the graph correspond to body parts that can independently translate and rotate in 3D as well as deform to capture pose-dependent shape variations. Pairwise potentials define a “stitching cost” for pulling the limbs apart, giving rise to the stitched puppet model (SPM). Unlike existing realistic 3D body models, the distributed representation facilitates inference by allowing the model to more effectively explore the space of poses, much like existing 2D pictorial structures models. We infer pose and body shape using a form of particle-based max-product belief propagation. This gives the SPM the realism of recent 3D body models with the computational advantages of part-based models. We apply the SPM to two challenging problems involving estimating human shape and pose from 3D data. The first is the FAUST mesh alignment challenge (http://faust.is.tue.mpg.de/), where ours is the first method to successfully align all 3D meshes. The second involves estimating pose and shape from crude visual hull representations of complex body movements.

pdf Extended Abstract poster code/project video DOI Project Page [BibTex]

Zuffi, S., Black, M. J. The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 3537-3546, June 2015 (inproceedings)

pdf Extended Abstract poster code/project video DOI Project Page [BibTex]

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Displets: Resolving Stereo Ambiguities using Object Knowledge

Güney, F., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, June 2015 (inproceedings)

Abstract
Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation.

pdf abstract suppmat [BibTex]

Güney, F., Geiger, A. Displets: Resolving Stereo Ambiguities using Object Knowledge In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, June 2015 (inproceedings)

pdf abstract suppmat [BibTex]

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Object Scene Flow for Autonomous Vehicles

Menze, M., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, June 2015 (inproceedings)

Abstract
This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which can't be handled by existing methods.

pdf abstract suppmat DOI [BibTex]

Menze, M., Geiger, A. Object Scene Flow for Autonomous Vehicles In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, June 2015 (inproceedings)

pdf abstract suppmat DOI [BibTex]

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Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction

Akhter, I., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 1446-1455, June 2015 (inproceedings)

Abstract
The estimation of 3D human pose from 2D joint locations is central to many vision problems involving the analysis of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collected a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. The dataset and the prior will be made publicly available. Second, we define a general parameterization of body pose and a new, multistage, method to estimate 3D pose from 2D joint locations that uses an over-complete dictionary of human poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results on manual annotations on real images and automatic part-based detections on the Leeds sports pose dataset.

pdf Extended Abstract video project/data/code poster DOI Project Page Project Page [BibTex]

Akhter, I., Black, M. J. Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 1446-1455, June 2015 (inproceedings)

pdf Extended Abstract video project/data/code poster DOI Project Page Project Page [BibTex]

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Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers

Wulff, J., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 120-130, June 2015 (inproceedings)

Abstract
We address the elusive goal of estimating optical flow both accurately and efficiently by adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields. We learn the principal components of natural flow fields using flow computed from four Hollywood movies. Optical flow fields are then compactly approximated as a weighted sum of the basis flow fields. Our new PCA-Flow algorithm robustly estimates these weights from sparse feature matches. The method runs in under 300ms/frame on the MPI-Sintel dataset using a single CPU and is more accurate and significantly faster than popular methods such as LDOF and Classic+NL. The results, however, are too smooth for some applications. Consequently, we develop a novel sparse layered flow method in which each layer is represented by PCA-flow. Unlike existing layered methods, estimation is fast because it uses only sparse matches. We combine information from different layers into a dense flow field using an image-aware MRF. The resulting PCA-Layers method runs in 3.6s/frame, is significantly more accurate than PCA-flow and achieves state-of-the-art performance in occluded regions on MPI-Sintel.

pdf Extended Abstract Supplemental Material Poster Code Project Page Project Page [BibTex]

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Permutohedral Lattice CNNs

Kiefel, M., Jampani, V., Gehler, P. V.

In ICLR Workshop Track, May 2015 (inproceedings)

Abstract
This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.

pdf link (url) [BibTex]

Kiefel, M., Jampani, V., Gehler, P. V. Permutohedral Lattice CNNs In ICLR Workshop Track, May 2015 (inproceedings)

pdf link (url) [BibTex]

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Consensus Message Passing for Layered Graphical Models

Jampani, V., Eslami, S. M. A., Tarlow, D., Kohli, P., Winn, J.

In Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 38, pages: 425-433, JMLR Workshop and Conference Proceedings, May 2015 (inproceedings)

Abstract
Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing 'consensus' messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.

online pdf supplementary link (url) [BibTex]

Jampani, V., Eslami, S. M. A., Tarlow, D., Kohli, P., Winn, J. Consensus Message Passing for Layered Graphical Models In Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 38, pages: 425-433, JMLR Workshop and Conference Proceedings, May 2015 (inproceedings)

online pdf supplementary link (url) [BibTex]

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Shape Models of the Human Body for Distributed Inference

Zuffi, S.

Brown University, May 2015 (phdthesis)

Abstract
In this thesis we address the problem of building shape models of the human body, in 2D and 3D, which are realistic and efficient to use. We focus our efforts on the human body, which is highly articulated and has interesting shape variations, but the approaches we present here can be applied to generic deformable and articulated objects. To address efficiency, we constrain our models to be part-based and have a tree-structured representation with pairwise relationships between connected parts. This allows the application of methods for distributed inference based on message passing. To address realism, we exploit recent advances in computer graphics that represent the human body with statistical shape models learned from 3D scans. We introduce two articulated body models, a 2D model, named Deformable Structures (DS), which is a contour-based model parameterized for 2D pose and projected shape, and a 3D model, named Stitchable Puppet (SP), which is a mesh-based model parameterized for 3D pose, pose-dependent deformations and intrinsic body shape. We have successfully applied the models to interesting and challenging problems in computer vision and computer graphics, namely pose estimation from static images, pose estimation from video sequences, pose and shape estimation from 3D scan data. This advances the state of the art in human pose and shape estimation and suggests that carefully de ned realistic models can be important for computer vision. More work at the intersection of vision and graphics is thus encouraged.

PDF [BibTex]

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Multi-view and 3D Deformable Part Models

Pepik, B., Stark, M., Gehler, P., Schiele, B.

Pattern Analysis and Machine Intelligence, 37(11):14, IEEE, March 2015 (article)

Abstract
As objects are inherently 3-dimensional, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2], 3D object classes [3], Pascal3D+ [4], Pascal VOC 2007 [5], EPFL multi-view cars [6]).

DOI Project Page [BibTex]

Pepik, B., Stark, M., Gehler, P., Schiele, B. Multi-view and 3D Deformable Part Models Pattern Analysis and Machine Intelligence, 37(11):14, IEEE, March 2015 (article)

DOI Project Page [BibTex]

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From Scans to Models: Registration of 3D Human Shapes Exploiting Texture Information

Bogo, F.

University of Padova, March 2015 (phdthesis)

Abstract
New scanning technologies are increasing the importance of 3D mesh data, and of algorithms that can reliably register meshes obtained from multiple scans. Surface registration is important e.g. for building full 3D models from partial scans, identifying and tracking objects in a 3D scene, creating statistical shape models. Human body registration is particularly important for many applications, ranging from biomedicine and robotics to the production of movies and video games; but obtaining accurate and reliable registrations is challenging, given the articulated, non-rigidly deformable structure of the human body. In this thesis, we tackle the problem of 3D human body registration. We start by analyzing the current state of the art, and find that: a) most registration techniques rely only on geometric information, which is ambiguous on flat surface areas; b) there is a lack of adequate datasets and benchmarks in the field. We address both issues. Our contribution is threefold. First, we present a model-based registration technique for human meshes that combines geometry and surface texture information to provide highly accurate mesh-to-mesh correspondences. Our approach estimates scene lighting and surface albedo, and uses the albedo to construct a high-resolution textured 3D body model that is brought into registration with multi-camera image data using a robust matching term. Second, by leveraging our technique, we present FAUST (Fine Alignment Using Scan Texture), a novel dataset collecting 300 high-resolution scans of 10 people in a wide range of poses. FAUST is the first dataset providing both real scans and automatically computed, reliable "ground-truth" correspondences between them. Third, we explore possible uses of our approach in dermatology. By combining our registration technique with a melanocytic lesion segmentation algorithm, we propose a system that automatically detects new or evolving lesions over almost the entire body surface, thus helping dermatologists identify potential melanomas. We conclude this thesis investigating the benefits of using texture information to establish frame-to-frame correspondences in dynamic monocular sequences captured with consumer depth cameras. We outline a novel approach to reconstruct realistic body shape and appearance models from dynamic human performances, and show preliminary results on challenging sequences captured with a Kinect.

[BibTex]

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Active Learning for Abstract Models of Collectives

Schiendorfer, A., Lassner, C., Anders, G., Reif, W., Lienhart, R.

In 3rd Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), March 2015 (inproceedings)

Abstract
Organizational structures such as hierarchies provide an effective means to deal with the increasing complexity found in large-scale energy systems. In hierarchical systems, the concrete functions describing the subsystems can be replaced by abstract piecewise linear functions to speed up the optimization process. However, if the data points are weakly informative the resulting abstracted optimization problem introduces severe errors and exhibits bad runtime performance. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Therefore, we propose to apply methods from active learning to search for informative inputs. We present first results experimenting with Decision Forests and Gaussian Processes that motivate further research. Using points selected by Decision Forests, we could reduce the average mean-squared error of the abstract piecewise linear function by one third.

code (hosted on github) pdf [BibTex]

Schiendorfer, A., Lassner, C., Anders, G., Reif, W., Lienhart, R. Active Learning for Abstract Models of Collectives In 3rd Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), March 2015 (inproceedings)

code (hosted on github) pdf [BibTex]

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Long Range Motion Estimation and Applications

Sevilla-Lara, L.

Long Range Motion Estimation and Applications, University of Massachusetts Amherst, University of Massachusetts Amherst, Febuary 2015 (phdthesis)

Abstract
Finding correspondences between images underlies many computer vision problems, such as optical flow, tracking, stereovision and alignment. Finding these correspondences involves formulating a matching function and optimizing it. This optimization process is often gradient descent, which avoids exhaustive search, but relies on the assumption of being in the basin of attraction of the right local minimum. This is often the case when the displacement is small, and current methods obtain very accurate results for small motions. However, when the motion is large and the matching function is bumpy this assumption is less likely to be true. One traditional way of avoiding this abruptness is to smooth the matching function spatially by blurring the images. As the displacement becomes larger, the amount of blur required to smooth the matching function becomes also larger. This averaging of pixels leads to a loss of detail in the image. Therefore, there is a trade-off between the size of the objects that can be tracked and the displacement that can be captured. In this thesis we address the basic problem of increasing the size of the basin of attraction in a matching function. We use an image descriptor called distribution fields (DFs). By blurring the images in DF space instead of in pixel space, we in- crease the size of the basin attraction with respect to traditional methods. We show competitive results using DFs both in object tracking and optical flow. Finally we demonstrate an application of capturing large motions for temporal video stitching.

[BibTex]

Sevilla-Lara, L. Long Range Motion Estimation and Applications Long Range Motion Estimation and Applications, University of Massachusetts Amherst, University of Massachusetts Amherst, Febuary 2015 (phdthesis)

[BibTex]