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44 results (View BibTeX file of all listed publications)

2018

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Customized Multi-Person Tracker

Ma, L., Tang, S., Black, M. J., Gool, L. V.

In Computer Vision – ACCV 2018, Springer International Publishing, December 2018 (inproceedings)

PDF Project Page [BibTex]

2018

Ma, L., Tang, S., Black, M. J., Gool, L. V. Customized Multi-Person Tracker In Computer Vision – ACCV 2018, Springer International Publishing, December 2018 (inproceedings)

PDF Project Page [BibTex]

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On the Integration of Optical Flow and Action Recognition

Sevilla-Lara, L., Liao, Y., Güney, F., Jampani, V., Geiger, A., Black, M. J.

In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 281-297, Springer, Cham, October 2018 (inproceedings)

Abstract
Most of the top performing action recognition methods use optical flow as a "black box" input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: 1) optical flow is useful for action recognition because it is invariant to appearance, 2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, 3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, 4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and 5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.

arXiv DOI [BibTex]

Sevilla-Lara, L., Liao, Y., Güney, F., Jampani, V., Geiger, A., Black, M. J. On the Integration of Optical Flow and Action Recognition In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 281-297, Springer, Cham, October 2018 (inproceedings)

arXiv DOI [BibTex]

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Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation

Wulff, J., Black, M. J.

In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 567-582, Springer, Cham, October 2018 (inproceedings)

Abstract
The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervised methods require heuristic n losses. Proxy tasks can overcome these issues, and start by training a network for a task for which annotation is easier or which can be trained unsupervised. The trained network is then fine-tuned for the original task using small amounts of ground truth data. Here, we investigate frame interpolation as a proxy task for optical flow. Using real movies, we train a CNN unsupervised for temporal interpolation. Such a network implicitly estimates motion, but cannot handle untextured regions. By fi ne-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fi elds. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.

pdf arXiv DOI Project Page [BibTex]

Wulff, J., Black, M. J. Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation In German Conference on Pattern Recognition (GCPR), LNCS 11269, pages: 567-582, Springer, Cham, October 2018 (inproceedings)

pdf arXiv DOI Project Page [BibTex]

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Human Motion Parsing by Hierarchical Dynamic Clustering

Zhang, Y., Tang, S., Sun, H., Neumann, H.

In Proceedings of the British Machine Vision Conference (BMVC), pages: 269, BMVA Press, September 2018 (inproceedings)

Abstract
Parsing continuous human motion into meaningful segments plays an essential role in various applications. In this work, we propose a hierarchical dynamic clustering framework to derive action clusters from a sequence of local features in an unsuper- vised bottom-up manner. We systematically investigate the modules in this framework and particularly propose diverse temporal pooling schemes, in order to realize accurate temporal action localization. We demonstrate our method on two motion parsing tasks: temporal action segmentation and abnormal behavior detection. The experimental results indicate that the proposed framework is significantly more effective than the other related state-of-the-art methods on several datasets.

pdf Project Page [BibTex]

Zhang, Y., Tang, S., Sun, H., Neumann, H. Human Motion Parsing by Hierarchical Dynamic Clustering In Proceedings of the British Machine Vision Conference (BMVC), pages: 269, BMVA Press, September 2018 (inproceedings)

pdf Project Page [BibTex]

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Generating 3D Faces using Convolutional Mesh Autoencoders

Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)

Abstract
Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://coma.is.tue.mpg.de/.

Code (tensorflow) Code (pytorch) Project Page paper supplementary DOI Project Page Project Page [BibTex]

Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J. Generating 3D Faces using Convolutional Mesh Autoencoders In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)

Code (tensorflow) Code (pytorch) Project Page paper supplementary DOI Project Page Project Page [BibTex]

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Part-Aligned Bilinear Representations for Person Re-identification

Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K. M.

In European Conference on Computer Vision (ECCV), 11218, pages: 418-437, Springer, Cham, September 2018 (inproceedings)

Abstract
Comparing the appearance of corresponding body parts is essential for person re-identification. However, body parts are frequently misaligned be- tween detected boxes, due to the detection errors and the pose/viewpoint changes. In this paper, we propose a network that learns a part-aligned representation for person re-identification. Our model consists of a two-stream network, which gen- erates appearance and body part feature maps respectively, and a bilinear-pooling layer that fuses two feature maps to an image descriptor. We show that it results in a compact descriptor, where the inner product between two image descriptors is equivalent to an aggregation of the local appearance similarities of the cor- responding body parts, and thereby significantly reduces the part misalignment problem. Our approach is advantageous over other pose-guided representations by learning part descriptors optimal for person re-identification. Training the net- work does not require any part annotation on the person re-identification dataset. Instead, we simply initialize the part sub-stream using a pre-trained sub-network of an existing pose estimation network and train the whole network to minimize the re-identification loss. We validate the effectiveness of our approach by demon- strating its superiority over the state-of-the-art methods on the standard bench- mark datasets including Market-1501, CUHK03, CUHK01 and DukeMTMC, and standard video dataset MARS.

pdf supplementary DOI Project Page [BibTex]

Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K. M. Part-Aligned Bilinear Representations for Person Re-identification In European Conference on Computer Vision (ECCV), 11218, pages: 418-437, Springer, Cham, September 2018 (inproceedings)

pdf supplementary DOI Project Page [BibTex]

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Learning Human Optical Flow

Ranjan, A., Romero, J., Black, M. J.

In 29th British Machine Vision Conference, September 2018 (inproceedings)

Abstract
The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.

video code pdf link (url) Project Page Project Page [BibTex]

Ranjan, A., Romero, J., Black, M. J. Learning Human Optical Flow In 29th British Machine Vision Conference, September 2018 (inproceedings)

video code pdf link (url) Project Page Project Page [BibTex]

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Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

(Best Student Paper Award)

Omran, M., Lassner, C., Pons-Moll, G., Gehler, P. V., Schiele, B.

In 3DV, September 2018 (inproceedings)

Abstract
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is difficult: perspective ambiguities make the loss function noisy and training data is scarce. In this paper, we propose a novel approach (Neural Body Fitting (NBF)). It integrates a statistical body model within a CNN, leveraging reliable bottom-up semantic body part segmentation and robust top-down body model constraints. NBF is fully differentiable and can be trained using 2D and 3D annotations. In detailed experiments, we analyze how the components of our model affect performance, especially the use of part segmentations as an explicit intermediate representation, and present a robust, efficiently trainable framework for 3D human pose estimation from 2D images with competitive results on standard benchmarks. Code is available at https://github.com/mohomran/neural_body_fitting

arXiv code Project Page [BibTex]

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Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat Video Project Page DOI Project Page [BibTex]

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat Video Project Page DOI Project Page [BibTex]

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Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis

Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Muller-Felber, W., Schroeder, A. S.

In Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018 (inproceedings)

Abstract
Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D full-body movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL) from noisy, low-quality, and incomplete RGB-D data. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA.

pdf Project page video extended arXiv version DOI Project Page [BibTex]

Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Muller-Felber, W., Schroeder, A. S. Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis In Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018 (inproceedings)

pdf Project page video extended arXiv version DOI Project Page [BibTex]

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Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Prokudin, S., Gehler, P., Nowozin, S.

European Conference on Computer Vision (ECCV), September 2018 (conference)

Abstract
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable we would like our models to quantify their uncertainty in order to achieve robustness against images of varying quality. Probabilistic deep learning models combine the expressive power of deep learning with uncertainty quantification. In this paper, we propose a novel probabilistic deep learning model for the task of angular regression. Our model uses von Mises distributions to predict a distribution over object pose angle. Whereas a single von Mises distribution is making strong assumptions about the shape of the distribution, we extend the basic model to predict a mixture of von Mises distributions. We show how to learn a mixture model using a finite and infinite number of mixture components. Our model allow for likelihood-based training and efficient inference at test time. We demonstrate on a number of challenging pose estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the current state-of-the-art.

code pdf [BibTex]

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Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

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

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11214, pages: 614-631, Springer, Cham, September 2018 (inproceedings)

Abstract
In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild. This poses many new challenges: the moving camera, heading drift, cluttered background, occlusions and many people visible in the video. We associate 2D pose detections in each image to the corresponding IMU-equipped persons by solving a novel graph based optimization problem that forces 3D to 2D coherency within a frame and across long range frames. Given associations, we jointly optimize the pose of a statistical body model, the camera pose and heading drift using a continuous optimization framework. We validated our method on the TotalCapture dataset, which provides video and IMU synchronized with ground truth. We obtain an accuracy of 26mm, which makes it accurate enough to serve as a benchmark for image-based 3D pose estimation in the wild. Using our method, we recorded 3D Poses in the Wild (3DPW ), a new dataset consisting of more than 51; 000 frames with accurate 3D pose in challenging sequences, including walking in the city, going up-stairs, having co ffee or taking the bus. We make the reconstructed 3D poses, video, IMU and 3D models available for research purposes at http://virtualhumans.mpi-inf.mpg.de/3DPW.

pdf SupMat data project DOI Project Page [BibTex]

Marcard, T. V., Henschel, R., Black, M. J., Rosenhahn, B., Pons-Moll, G. Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11214, pages: 614-631, Springer, Cham, September 2018 (inproceedings)

pdf SupMat data project DOI Project Page [BibTex]

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Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju, R., Rajappa, S., Black, M. J., Karlapalem, K., Ahmad, A.

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-8, IEEE, August 2018 (conference)

Abstract
In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies nonconvex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies for different scenarios to showcase the convergence and efficacy of the proposed algorithm.

Published Version link (url) DOI [BibTex]

Tallamraju, R., Rajappa, S., Black, M. J., Karlapalem, K., Ahmad, A. Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-8, IEEE, August 2018 (conference)

Published Version link (url) DOI [BibTex]

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End-to-end Recovery of Human Shape and Pose

Kanazawa, A., Black, M. J., Jacobs, D. W., Malik, J.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

Abstract
We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, we produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allows our model to be trained using in-the-wild images that only have ground truth 2D annotations. However, the reprojection loss alone is highly underconstrained. In this work we address this problem by introducing an adversary trained to tell whether human body shape and pose parameters are real or not using a large database of 3D human meshes. We show that HMR can be trained with and without using any paired 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detections and infer 3D pose and shape parameters directly from image pixels. Our model runs in real-time given a bounding box containing the person. We demonstrate our approach on various images in-the-wild and out-perform previous optimization-based methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.

pdf code project video Project Page [BibTex]

Kanazawa, A., Black, M. J., Jacobs, D. W., Malik, J. End-to-end Recovery of Human Shape and Pose In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

pdf code project video Project Page [BibTex]

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Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images

Zuffi, S., Kanazawa, A., Black, M. J.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

Abstract
Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.

pdf code/data 3D models Project Page [BibTex]

Zuffi, S., Kanazawa, A., Black, M. J. Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

pdf code/data 3D models 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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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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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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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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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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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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Efficient Facade Segmentation using Auto-Context

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

In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pages: 1038-1045, IEEE, January 2015 (inproceedings)

Abstract
In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using auto-context features and learned using the stacked generalization technique. We find that this, albeit standard, technique performs better, or equals, all previous published empirical results on all available facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test time inference.

website pdf supplementary IEEE page link (url) DOI Project Page [BibTex]

Jampani, V., Gadde, R., Gehler, P. V. Efficient Facade Segmentation using Auto-Context In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pages: 1038-1045, IEEE, January 2015 (inproceedings)

website pdf supplementary IEEE page link (url) DOI Project Page [BibTex]

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FlowCap: 2D Human Pose from Optical Flow

Romero, J., Loper, M., Black, M. J.

In Pattern Recognition, Proc. 37th German Conference on Pattern Recognition (GCPR), LNCS 9358, pages: 412-423, Springer, 2015 (inproceedings)

Abstract
We estimate 2D human pose from video using only optical flow. The key insight is that dense optical flow can provide information about 2D body pose. Like range data, flow is largely invariant to appearance but unlike depth it can be directly computed from monocular video. We demonstrate that body parts can be detected from dense flow using the same random forest approach used by the Microsoft Kinect. Unlike range data, however, when people stop moving, there is no optical flow and they effectively disappear. To address this, our FlowCap method uses a Kalman filter to propagate body part positions and ve- locities over time and a regression method to predict 2D body pose from part centers. No range sensor is required and FlowCap estimates 2D human pose from monocular video sources containing human motion. Such sources include hand-held phone cameras and archival television video. We demonstrate 2D body pose estimation in a range of scenarios and show that the method works with real-time optical flow. The results suggest that optical flow shares invariances with range data that, when complemented with tracking, make it valuable for pose estimation.

video pdf preprint Project Page Project Page [BibTex]

Romero, J., Loper, M., Black, M. J. FlowCap: 2D Human Pose from Optical Flow In Pattern Recognition, Proc. 37th German Conference on Pattern Recognition (GCPR), LNCS 9358, pages: 412-423, Springer, 2015 (inproceedings)

video pdf preprint Project Page Project Page [BibTex]

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Joint 3D Object and Layout Inference from a single RGB-D Image

(Best Paper Award)

Geiger, A., Wang, C.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)

Abstract
Inferring 3D objects and the layout of indoor scenes from a single RGB-D image captured with a Kinect camera is a challenging task. Towards this goal, we propose a high-order graphical model and jointly reason about the layout, objects and superpixels in the image. In contrast to existing holistic approaches, our model leverages detailed 3D geometry using inverse graphics and explicitly enforces occlusion and visibility constraints for respecting scene properties and projective geometry. We cast the task as MAP inference in a factor graph and solve it efficiently using message passing. We evaluate our method with respect to several baselines on the challenging NYUv2 indoor dataset using 21 object categories. Our experiments demonstrate that the proposed method is able to infer scenes with a large degree of clutter and occlusions.

pdf suppmat video project DOI [BibTex]

Geiger, A., Wang, C. Joint 3D Object and Layout Inference from a single RGB-D Image In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)

pdf suppmat video project DOI [BibTex]

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3D Object Class Detection in the Wild

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

In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2015 (inproceedings)

Project Page [BibTex]

Pepik, B., Stark, M., Gehler, P., Ritschel, T., Schiele, B. 3D Object Class Detection in the Wild In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2015 (inproceedings)

Project Page [BibTex]

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Discrete Optimization for Optical Flow

Menze, M., Heipke, C., Geiger, A.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)

Abstract
We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naive discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of discrete optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI.

pdf suppmat project DOI [BibTex]

Menze, M., Heipke, C., Geiger, A. Discrete Optimization for Optical Flow In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)

pdf suppmat project DOI [BibTex]

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Joint 3D Estimation of Vehicles and Scene Flow

Menze, M., Heipke, C., Geiger, A.

In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)

Abstract
Three-dimensional reconstruction of dynamic scenes is an important prerequisite for applications like mobile robotics or autonomous driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.

PDF [BibTex]

Menze, M., Heipke, C., Geiger, A. Joint 3D Estimation of Vehicles and Scene Flow In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)

PDF [BibTex]

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Smooth Loops from Unconstrained Video

Sevilla-Lara, L., Wulff, J., Sunkavalli, K., Shechtman, E.

In Computer Graphics Forum (Proceedings of EGSR), 34(4):99-107, 2015 (inproceedings)

Abstract
Converting unconstrained video sequences into videos that loop seamlessly is an extremely challenging problem. In this work, we take the first steps towards automating this process by focusing on an important subclass of videos containing a single dominant foreground object. Our technique makes two novel contributions over previous work: first, we propose a correspondence-based similarity metric to automatically identify a good transition point in the video where the appearance and dynamics of the foreground are most consistent. Second, we develop a technique that aligns both the foreground and background about this transition point using a combination of global camera path planning and patch-based video morphing. We demonstrate that this allows us to create natural, compelling, loopy videos from a wide range of videos collected from the internet.

pdf link (url) DOI Project Page [BibTex]

Sevilla-Lara, L., Wulff, J., Sunkavalli, K., Shechtman, E. Smooth Loops from Unconstrained Video In Computer Graphics Forum (Proceedings of EGSR), 34(4):99-107, 2015 (inproceedings)

pdf link (url) DOI Project Page [BibTex]

2011

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Home 3D body scans from noisy image and range data

Weiss, A., Hirshberg, D., Black, M.

In Int. Conf. on Computer Vision (ICCV), pages: 1951-1958, IEEE, Barcelona, November 2011 (inproceedings)

Abstract
The 3D shape of the human body is useful for applications in fitness, games and apparel. Accurate body scanners, however, are expensive, limiting the availability of 3D body models. We present a method for human shape reconstruction from noisy monocular image and range data using a single inexpensive commodity sensor. The approach combines low-resolution image silhouettes with coarse range data to estimate a parametric model of the body. Accurate 3D shape estimates are obtained by combining multiple monocular views of a person moving in front of the sensor. To cope with varying body pose, we use a SCAPE body model which factors 3D body shape and pose variations. This enables the estimation of a single consistent shape while allowing pose to vary. Additionally, we describe a novel method to minimize the distance between the projected 3D body contour and the image silhouette that uses analytic derivatives of the objective function. We propose a simple method to estimate standard body measurements from the recovered SCAPE model and show that the accuracy of our method is competitive with commercial body scanning systems costing orders of magnitude more.

pdf YouTube poster Project Page Project Page [BibTex]

2011

Weiss, A., Hirshberg, D., Black, M. Home 3D body scans from noisy image and range data In Int. Conf. on Computer Vision (ICCV), pages: 1951-1958, IEEE, Barcelona, November 2011 (inproceedings)

pdf YouTube poster Project Page Project Page [BibTex]

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Evaluating the Automated Alignment of 3D Human Body Scans

Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B., Black, M. J.

In 2nd International Conference on 3D Body Scanning Technologies, pages: 76-86, (Editors: D’Apuzzo, Nicola), Hometrica Consulting, Lugano, Switzerland, October 2011 (inproceedings)

Abstract
The statistical analysis of large corpora of human body scans requires that these scans be in alignment, either for a small set of key landmarks or densely for all the vertices in the scan. Existing techniques tend to rely on hand-placed landmarks or algorithms that extract landmarks from scans. The former is time consuming and subjective while the latter is error prone. Here we show that a model-based approach can align meshes automatically, producing alignment accuracy similar to that of previous methods that rely on many landmarks. Specifically, we align a low-resolution, artist-created template body mesh to many high-resolution laser scans. Our alignment procedure employs a robust iterative closest point method with a regularization that promotes smooth and locally rigid deformation of the template mesh. We evaluate our approach on 50 female body models from the CAESAR dataset that vary significantly in body shape. To make the method fully automatic, we define simple feature detectors for the head and ankles, which provide initial landmark locations. We find that, if body poses are fairly similar, as in CAESAR, the fully automated method provides dense alignments that enable statistical analysis and anthropometric measurement.

pdf slides DOI Project Page [BibTex]

Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B., Black, M. J. Evaluating the Automated Alignment of 3D Human Body Scans In 2nd International Conference on 3D Body Scanning Technologies, pages: 76-86, (Editors: D’Apuzzo, Nicola), Hometrica Consulting, Lugano, Switzerland, October 2011 (inproceedings)

pdf slides DOI Project Page [BibTex]

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Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Gehler, P., Rother, C., Kiefel, M., Zhang, L., Schölkopf, B.

In Advances in Neural Information Processing Systems 24, pages: 765-773, (Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.), Curran Associates, Inc., Red Hook, NY, USA, 2011 (inproceedings)

Abstract
We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.

website + code pdf poster Project Page Project Page [BibTex]

Gehler, P., Rother, C., Kiefel, M., Zhang, L., Schölkopf, B. Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance In Advances in Neural Information Processing Systems 24, pages: 765-773, (Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.), Curran Associates, Inc., Red Hook, NY, USA, 2011 (inproceedings)

website + code pdf poster Project Page Project Page [BibTex]

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Combining wireless neural recording and video capture for the analysis of natural gait

Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M. J., Shenoy, K.

In Proc. 5th Int. IEEE EMBS Conf. on Neural Engineering, pages: 613-616, IEEE, 2011 (inproceedings)

pdf Project Page [BibTex]

Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M. J., Shenoy, K. Combining wireless neural recording and video capture for the analysis of natural gait In Proc. 5th Int. IEEE EMBS Conf. on Neural Engineering, pages: 613-616, IEEE, 2011 (inproceedings)

pdf Project Page [BibTex]

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Benchmark datasets for pose estimation and tracking

Andriluka, M., Sigal, L., Black, M. J.

In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)

publisher's site Project Page [BibTex]

Andriluka, M., Sigal, L., Black, M. J. Benchmark datasets for pose estimation and tracking In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)

publisher's site Project Page [BibTex]

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Shape and pose-invariant correspondences using probabilistic geodesic surface embedding

Tsoli, A., Black, M. J.

In 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM), 6835, pages: 256-265, Lecture Notes in Computer Science, (Editors: Mester, Rudolf and Felsberg, Michael), Springer, 2011 (inproceedings)

Abstract
Correspondence between non-rigid deformable 3D objects provides a foundation for object matching and retrieval, recognition, and 3D alignment. Establishing 3D correspondence is challenging when there are non-rigid deformations or articulations between instances of a class. We present a method for automatically finding such correspondences that deals with significant variations in pose, shape and resolution between pairs of objects.We represent objects as triangular meshes and consider normalized geodesic distances as representing their intrinsic characteristics. Geodesic distances are invariant to pose variations and nearly invariant to shape variations when properly normalized. The proposed method registers two objects by optimizing a joint probabilistic model over a subset of vertex pairs between the objects. The model enforces preservation of geodesic distances between corresponding vertex pairs and inference is performed using loopy belief propagation in a hierarchical scheme. Additionally our method prefers solutions in which local shape information is consistent at matching vertices. We quantitatively evaluate our method and show that is is more accurate than a state of the art method.

pdf talk Project Page [BibTex]

Tsoli, A., Black, M. J. Shape and pose-invariant correspondences using probabilistic geodesic surface embedding In 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM), 6835, pages: 256-265, Lecture Notes in Computer Science, (Editors: Mester, Rudolf and Felsberg, Michael), Springer, 2011 (inproceedings)

pdf talk Project Page [BibTex]

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Steerable random fields for image restoration and inpainting

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.

publisher site [BibTex]

Roth, S., Black, M. J. Steerable random fields for image restoration and inpainting In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

publisher site [BibTex]