Publications | Perceiving Systems - Max Planck Institute for Intelligent Systems

48 results (View BibTeX file of all listed publications)

2018

Customized Multi-Person Tracker

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

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

2018

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

PDF Project Page [BibTex]

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]

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 fine-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fields. 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]

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]

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]

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]

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]

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]

Omran, M., Lassner, C., Pons-Moll, G., Gehler, P. V., Schiele, B. Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation In 3DV, September 2018 (inproceedings)

arXiv code Project Page [BibTex]

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]

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]

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]

Prokudin, S., Gehler, P., Nowozin, S. Deep Directional Statistics: Pose Estimation with Uncertainty Quantification European Conference on Computer Vision (ECCV), September 2018 (conference)

code pdf [BibTex]

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 coffee 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]

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]

Model-based Optical Flow: Layers, Learning, and Geometry

Wulff, J.

Tuebingen University, April 2018 (phdthesis)

Abstract

The estimation of motion in video sequences establishes temporal correspondences between pixels and surfaces and allows reasoning about a scene using multiple frames. Despite being a focus of research for over three decades, computing motion, or optical flow, remains challenging due to a number of difficulties, including the treatment of motion discontinuities and occluded regions, and the integration of information from more than two frames. One reason for these issues is that most optical flow algorithms only reason about the motion of pixels on the image plane, while not taking the image formation pipeline or the 3D structure of the world into account. One approach to address this uses layered models, which represent the occlusion structure of a scene and provide an approximation to the geometry. The goal of this dissertation is to show ways to inject additional knowledge about the scene into layered methods, making them more robust, faster, and more accurate. First, this thesis demonstrates the modeling power of layers using the example of motion blur in videos, which is caused by fast motion relative to the exposure time of the camera. Layers segment the scene into regions that move coherently while preserving their occlusion relationships. The motion of each layer therefore directly determines its motion blur. At the same time, the layered model captures complex blur overlap effects at motion discontinuities. Using layers, we can thus formulate a generative model for blurred video sequences, and use this model to simultaneously deblur a video and compute accurate optical flow for highly dynamic scenes containing motion blur. Next, we consider the representation of the motion within layers. Since, in a layered model, important motion discontinuities are captured by the segmentation into layers, the flow within each layer varies smoothly and can be approximated using a low dimensional subspace. We show how this subspace can be learned from training data using principal component analysis (PCA), and that flow estimation using this subspace is computationally efficient. The combination of the layered model and the low-dimensional subspace gives the best of both worlds, sharp motion discontinuities from the layers and computational efficiency from the subspace. Lastly, we show how layered methods can be dramatically improved using simple semantics. Instead of treating all layers equally, a semantic segmentation divides the scene into its static parts and moving objects. Static parts of the scene constitute a large majority of what is shown in typical video sequences; yet, in such regions optical flow is fully constrained by the depth structure of the scene and the camera motion. After segmenting out moving objects, we consider only static regions, and explicitly reason about the structure of the scene and the camera motion, yielding much better optical flow estimates. Furthermore, computing the structure of the scene allows to better combine information from multiple frames, resulting in high accuracies even in occluded regions. For moving regions, we compute the flow using a generic optical flow method, and combine it with the flow computed for the static regions to obtain a full optical flow field. By combining layered models of the scene with reasoning about the dynamic behavior of the real, three-dimensional world, the methods presented herein push the envelope of optical flow computation in terms of robustness, speed, and accuracy, giving state-of-the-art results on benchmarks and pointing to important future research directions for the estimation of motion in natural scenes.

Official link DOI Project Page [BibTex]

Wulff, J. Model-based Optical Flow: Layers, Learning, and Geometry Tuebingen University, April 2018 (phdthesis)

Official link DOI Project Page [BibTex]

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]

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]

PoTion: Pose MoTion Representation for Action Recognition

Choutas, V., Weinzaepfel, P., Revaud, J., Schmid, C.

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

Abstract

Most state-of-the-art methods for action recognition rely on a two-stream architecture that processes appearance and motion independently. In this paper, we claim that consider- ing them jointly offers rich information for action recogni- tion. We introduce a novel representation that gracefully en- codes the movement of some semantic keypoints. We use the human joints as these keypoints and term our Pose moTion representation PoTion. Specifically, we first run a state- of-the-art human pose estimator [4] and extract heatmaps for the human joints in each frame. We obtain our PoTion representation by temporally aggregating these probability maps. This is achieved by ‘colorizing’ each of them de- pending on the relative time of the frames in the video clip and summing them. This fixed-size representation for an en- tire video clip is suitable to classify actions using a shallow convolutional neural network. Our experimental evaluation shows that PoTion outper- forms other state-of-the-art pose representations [6, 48]. Furthermore, it is complementary to standard appearance and motion streams. When combining PoTion with the recent two-stream I3D approach [5], we obtain state-of- the-art performance on the JHMDB, HMDB and UCF101 datasets.

PDF [BibTex]

Choutas, V., Weinzaepfel, P., Revaud, J., Schmid, C. PoTion: Pose MoTion Representation for Action Recognition In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2018 (inproceedings)

PDF [BibTex]

2008

Learning Optical Flow

Sun, D., Roth, S., Lewis, J., Black, M. J.

In European Conf. on Computer Vision, ECCV, 5304, pages: 83-97, LNCS, (Editors: Forsyth, D. and Torr, P. and Zisserman, A.), Springer-Verlag, October 2008 (inproceedings)

Abstract

Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical model of both brightness constancy error and the spatial properties of optical flow using image sequences with associated ground truth flow fields. The result is a complete probabilistic model of optical flow. Specifically, the ground truth enables us to model how the assumption of brightness constancy is violated in naturalistic sequences, resulting in a probabilistic model of "brightness inconstancy". We also generalize previous high-order constancy assumptions, such as gradient constancy, by modeling the constancy of responses to various linear filters in a high-order random field framework. These filters are free variables that can be learned from training data. Additionally we study the spatial structure of the optical flow and how motion boundaries are related to image intensity boundaries. Spatial smoothness is modeled using a Steerable Random Field, where spatial derivatives of the optical flow are steered by the image brightness structure. These models provide a statistical motivation for previous methods and enable the learning of all parameters from training data. All proposed models are quantitatively compared on the Middlebury flow dataset.

pdf Springerlink version [BibTex]

2008

Sun, D., Roth, S., Lewis, J., Black, M. J. Learning Optical Flow In European Conf. on Computer Vision, ECCV, 5304, pages: 83-97, LNCS, (Editors: Forsyth, D. and Torr, P. and Zisserman, A.), Springer-Verlag, October 2008 (inproceedings)

pdf Springerlink version [BibTex]

Probabilistic Roadmap Method and Real Time Gait Changing Technique Implementation for Travel Time Optimization on a Designed Six-legged Robot

Ahmad, A., Dhang, N.

In pages: 1-5, October 2008 (inproceedings)

Abstract

This paper presents design and development of a six legged robot with a total of 12 degrees of freedom, two in each limb and then an implementation of 'obstacle and undulated terrain-based' probabilistic roadmap method for motion planning of this hexaped which is able to negotiate large undulations as obstacles. The novelty in this implementation is that, it doesnt require the complete view of the robot's configuration space at any given time during the traversal. It generates a map of the area that is in visibility range and finds the best suitable point in that field of view to make it as the next node of the algorithm. A particular category of undulations which are small enough are automatically 'run-over' as a part of the terrain and not considered as obstacles. The traversal between the nodes is optimized by taking the shortest path and the most optimum gait at that instance which the hexaped can assume. This is again a novel approach to have a real time gait changing technique to optimize the travel time. The hexaped limb can swing in the robot's X-Y plane and the lower link of the limb can move in robot's Z plane by an implementation of a four-bar mechanism. A GUI based server 'Yellow Ladybird' eventually which is the name of the hexaped, is made for real time monitoring and communicating to it the final destination co-ordinates.

link (url) [BibTex]

Ahmad, A., Dhang, N. Probabilistic Roadmap Method and Real Time Gait Changing Technique Implementation for Travel Time Optimization on a Designed Six-legged Robot In pages: 1-5, October 2008 (inproceedings)

link (url) [BibTex]

The naked truth: Estimating body shape under clothing,

Balan, A., Black, M. J.

In European Conf. on Computer Vision, ECCV, 5304, pages: 15-29, LNCS, (Editors: D. Forsyth and P. Torr and A. Zisserman), Springer-Verlag, Marseilles, France, October 2008 (inproceedings)

Abstract

We propose a method to estimate the detailed 3D shape of a person from images of that person wearing clothing. The approach exploits a model of human body shapes that is learned from a database of over 2000 range scans. We show that the parameters of this shape model can be recovered independently of body pose. We further propose a generalization of the visual hull to account for the fact that observed silhouettes of clothed people do not provide a tight bound on the true 3D shape. With clothed subjects, different poses provide different constraints on the possible underlying 3D body shape. We consequently combine constraints across pose to more accurately estimate 3D body shape in the presence of occluding clothing. Finally we use the recovered 3D shape to estimate the gender of subjects and then employ gender-specific body models to refine our shape estimates. Results on a novel database of thousands of images of clothed and "naked" subjects, as well as sequences from the HumanEva dataset, suggest the method may be accurate enough for biometric shape analysis in video.

pdf pdf with higher quality images Springerlink version YouTube video on applications data slides [BibTex]

Balan, A., Black, M. J. The naked truth: Estimating body shape under clothing, In European Conf. on Computer Vision, ECCV, 5304, pages: 15-29, LNCS, (Editors: D. Forsyth and P. Torr and A. Zisserman), Springer-Verlag, Marseilles, France, October 2008 (inproceedings)

pdf pdf with higher quality images Springerlink version YouTube video on applications data slides [BibTex]

Dynamic time warping for binocular hand tracking and reconstruction

Romero, J., Kragic, D., Kyrki, V., Argyros, A.

In IEEE International Conference on Robotics and Automation,ICRA, pages: 2289 -2294, May 2008 (inproceedings)

Pdf [BibTex]

Romero, J., Kragic, D., Kyrki, V., Argyros, A. Dynamic time warping for binocular hand tracking and reconstruction In IEEE International Conference on Robotics and Automation,ICRA, pages: 2289 -2294, May 2008 (inproceedings)

Pdf [BibTex]

Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects

Kjellström, H., Romero, J., Martinez, D., Kragic, D.

In European Conference on Computer Vision, ECCV, pages: 336-349, 2008 (inproceedings)

Pdf [BibTex]

Kjellström, H., Romero, J., Martinez, D., Kragic, D. Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects In European Conference on Computer Vision, ECCV, pages: 336-349, 2008 (inproceedings)

Pdf [BibTex]

Tuning analysis of motor cortical neurons in a person with paralysis during performance of visually instructed cursor control tasks

Kim, S., Simeral, J. D., Hochberg, L. R., Truccolo, W., Donoghue, J., Friehs, G. M., Black, M. J.

2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Kim, S., Simeral, J. D., Hochberg, L. R., Truccolo, W., Donoghue, J., Friehs, G. M., Black, M. J. Tuning analysis of motor cortical neurons in a person with paralysis during performance of visually instructed cursor control tasks 2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Infinite Kernel Learning

Gehler, P., Nowozin, S.

In Proceedings of NIPS 2008 Workshop on "Kernel Learning: Automatic Selection of Optimal Kernels", 2008 (inproceedings)

project page pdf [BibTex]

Gehler, P., Nowozin, S. Infinite Kernel Learning In Proceedings of NIPS 2008 Workshop on "Kernel Learning: Automatic Selection of Optimal Kernels", 2008 (inproceedings)

project page pdf [BibTex]

Visual Recognition of Grasps for Human-to-Robot Mapping

Kjellström, H., Romero, J., Kragic, D.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pages: 3192-3199, 2008 (inproceedings)

Pdf [BibTex]

Kjellström, H., Romero, J., Kragic, D. Visual Recognition of Grasps for Human-to-Robot Mapping In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pages: 3192-3199, 2008 (inproceedings)

Pdf [BibTex]

More than two years of intracortically-based cursor control via a neural interface system

Hochberg, L. R., Simeral, J. D., Kim, S., Stein, J., Friehs, G. M., Black, M. J., Donoghue, J. P.

2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Hochberg, L. R., Simeral, J. D., Kim, S., Stein, J., Friehs, G. M., Black, M. J., Donoghue, J. P. More than two years of intracortically-based cursor control via a neural interface system 2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Decoding of reach and grasp from MI population spiking activity using a low-dimensional model of hand and arm posture

Yadollahpour, P., Shakhnarovich, G., Vargas-Irwin, C., Donoghue, J. P., Black, M. J.

2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Yadollahpour, P., Shakhnarovich, G., Vargas-Irwin, C., Donoghue, J. P., Black, M. J. Decoding of reach and grasp from MI population spiking activity using a low-dimensional model of hand and arm posture 2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Neural activity in the motor cortex of humans with tetraplegia

Donoghue, J., Simeral, J., Black, M., Kim, S., Truccolo, W., Hochberg, L.

AREADNE Research in Encoding And Decoding of Neural Ensembles, June, Santorini, Greece, 2008 (conference)

[BibTex]

Donoghue, J., Simeral, J., Black, M., Kim, S., Truccolo, W., Hochberg, L. Neural activity in the motor cortex of humans with tetraplegia AREADNE Research in Encoding And Decoding of Neural Ensembles, June, Santorini, Greece, 2008 (conference)

[BibTex]

Nonrigid Structure from Motion in Trajectory Space

Akhter, I., Sheikh, Y., Khan, S., Kanade, T.

In Neural Information Processing Systems, 1(2):41-48, 2008 (inproceedings)

Abstract

Existing approaches to nonrigid structure from motion assume that the instantaneous 3D shape of a deforming object is a linear combination of basis shapes, which have to be estimated anew for each video sequence. In contrast, we propose that the evolving 3D structure be described by a linear combination of basis trajectories. The principal advantage of this approach is that we do not need to estimate any basis vectors during computation. We show that generic bases over trajectories, such as the Discrete Cosine Transform (DCT) basis, can be used to compactly describe most real motions. This results in a significant reduction in unknowns, and corresponding stability in estimation. We report empirical performance, quantitatively using motion capture data, and qualitatively on several video sequences exhibiting nonrigid motions including piece-wise rigid motion, partially nonrigid motion (such as a facial expression), and highly nonrigid motion (such as a person dancing).

pdf project page [BibTex]

Akhter, I., Sheikh, Y., Khan, S., Kanade, T. Nonrigid Structure from Motion in Trajectory Space In Neural Information Processing Systems, 1(2):41-48, 2008 (inproceedings)

pdf project page [BibTex]

Combined discriminative and generative articulated pose and non-rigid shape estimation

Sigal, L., Balan, A., Black, M. J.

In Advances in Neural Information Processing Systems 20, NIPS-2007, pages: 1337–1344, MIT Press, 2008 (inproceedings)

pdf [BibTex]

Sigal, L., Balan, A., Black, M. J. Combined discriminative and generative articulated pose and non-rigid shape estimation In Advances in Neural Information Processing Systems 20, NIPS-2007, pages: 1337–1344, MIT Press, 2008 (inproceedings)

pdf [BibTex]

Reconstructing reach and grasp actions using neural population activity from Primary Motor Cortex

Vargas-Irwin, C. E., Yadollahpour, P., Shakhnarovich, G., Black, M. J., Donoghue, J. P.

2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

Vargas-Irwin, C. E., Yadollahpour, P., Shakhnarovich, G., Black, M. J., Donoghue, J. P. Reconstructing reach and grasp actions using neural population activity from Primary Motor Cortex 2008 Abstract Viewer and Itinerary Planner, Society for Neuroscience, Washington, DC, 2008, Online (conference)

[BibTex]

2004

Automatic spike sorting for neural decoding

Wood, F. D., Fellows, M., Donoghue, J. P., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 4009-4012, September 2004 (inproceedings)

pdf [BibTex]

2004

Wood, F. D., Fellows, M., Donoghue, J. P., Black, M. J. Automatic spike sorting for neural decoding In Proc. IEEE Engineering in Medicine and Biology Society, pages: 4009-4012, September 2004 (inproceedings)

pdf [BibTex]

Closed-loop neural control of cursor motion using a Kalman filter

Wu, W., Shaikhouni, A., Donoghue, J. P., Black, M. J.

In Proc. IEEE Engineering in Medicine and Biology Society, pages: 4126-4129, September 2004 (inproceedings)

pdf [BibTex]

Wu, W., Shaikhouni, A., Donoghue, J. P., Black, M. J. Closed-loop neural control of cursor motion using a Kalman filter In Proc. IEEE Engineering in Medicine and Biology Society, pages: 4126-4129, September 2004 (inproceedings)

pdf [BibTex]

The dense estimation of motion and appearance in layers

Yalcin, H., Black, M. J., Fablet, R.

In IEEE Workshop on Image and Video Registration, June 2004 (inproceedings)

pdf [BibTex]

Yalcin, H., Black, M. J., Fablet, R. The dense estimation of motion and appearance in layers In IEEE Workshop on Image and Video Registration, June 2004 (inproceedings)

pdf [BibTex]

3D human limb detection using space carving and multi-view eigen models

Bhatia, S., Sigal, L., Isard, M., Black, M. J.

In IEEE Workshop on Articulated and Nonrigid Motion, June 2004 (inproceedings)

pdf [BibTex]

Bhatia, S., Sigal, L., Isard, M., Black, M. J. 3D human limb detection using space carving and multi-view eigen models In IEEE Workshop on Articulated and Nonrigid Motion, June 2004 (inproceedings)

pdf [BibTex]

Tracking loose-limbed people

Sigal, L., Bhatia, S., Roth, S., Black, M. J., Isard, M.

In IEEE Conf. on Computer Vision and Pattern Recognition, 1, pages: 421-428, June 2004 (inproceedings)

pdf [BibTex]

Sigal, L., Bhatia, S., Roth, S., Black, M. J., Isard, M. Tracking loose-limbed people In IEEE Conf. on Computer Vision and Pattern Recognition, 1, pages: 421-428, June 2004 (inproceedings)

pdf [BibTex]

Gibbs likelihoods for Bayesian tracking

Roth, S., Sigal, L., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition, 1, pages: 886-893, June 2004 (inproceedings)

pdf [BibTex]

Roth, S., Sigal, L., Black, M. J. Gibbs likelihoods for Bayesian tracking In IEEE Conf. on Computer Vision and Pattern Recognition, 1, pages: 886-893, June 2004 (inproceedings)

pdf [BibTex]

A direct brain-machine interface for 2D cursor control using a Kalman filter

Shaikhouni, A., Wu, W., Moris, D. S., Donoghue, J. P., Black, M. J.

Society for Neuroscience, 2004, Online (conference)

abstract [BibTex]

Shaikhouni, A., Wu, W., Moris, D. S., Donoghue, J. P., Black, M. J. A direct brain-machine interface for 2D cursor control using a Kalman filter Society for Neuroscience, 2004, Online (conference)

abstract [BibTex]

1995

Robust estimation of multiple surface shapes from occluded textures

Black, M. J., Rosenholtz, R.

In International Symposium on Computer Vision, pages: 485-490, Miami, FL, November 1995 (inproceedings)

pdf [BibTex]

1995

Black, M. J., Rosenholtz, R. Robust estimation of multiple surface shapes from occluded textures In International Symposium on Computer Vision, pages: 485-490, Miami, FL, November 1995 (inproceedings)

pdf [BibTex]

The PLAYBOT Project

In Proc. IJCAI Workshop on AI Applications for Disabled People, Montreal, August 1995 (inproceedings)

abstract [BibTex]

Tsotsos, J. K., Dickinson, S., Jenkin, M., Milios, E., Jepson, A., Down, B., Amdur, E., Stevenson, S., Black, M., Metaxas, D., Cooperstock, J., Culhane, S., Nuflo, F., Verghese, G., Wai, W., Wilkes, D., Ye, Y. The PLAYBOT Project In Proc. IJCAI Workshop on AI Applications for Disabled People, Montreal, August 1995 (inproceedings)

abstract [BibTex]

Recognizing facial expressions under rigid and non-rigid facial motions using local parametric models of image motion

Black, M. J., Yacoob, Y.

In International Workshop on Automatic Face- and Gesture-Recognition, Zurich, July 1995 (inproceedings)

video abstract [BibTex]

Black, M. J., Yacoob, Y. Recognizing facial expressions under rigid and non-rigid facial motions using local parametric models of image motion In International Workshop on Automatic Face- and Gesture-Recognition, Zurich, July 1995 (inproceedings)

video abstract [BibTex]

Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion

Black, M. J., Yacoob, Y.

In Fifth International Conf. on Computer Vision, ICCV’95, pages: 347-381, Boston, MA, June 1995 (inproceedings)

Abstract

This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.

pdf video publisher site [BibTex]

Black, M. J., Yacoob, Y. Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion In Fifth International Conf. on Computer Vision, ICCV’95, pages: 347-381, Boston, MA, June 1995 (inproceedings)

pdf video publisher site [BibTex]

A computational model for shape from texture for multiple textures

Black, M. J., Rosenholtz, R.

Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, pages: 2202, March 1995 (conference)

abstract [BibTex]

Black, M. J., Rosenholtz, R. A computational model for shape from texture for multiple textures Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, pages: 2202, March 1995 (conference)

abstract [BibTex]

1992

Psychophysical implications of temporal persistence in early vision: A computational account of representational momentum

Tarr, M. J., Black, M. J.

Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, 33, pages: 1050, May 1992 (conference)

abstract [BibTex]

1992

Tarr, M. J., Black, M. J. Psychophysical implications of temporal persistence in early vision: A computational account of representational momentum Investigative Ophthalmology and Visual Science Supplement, Vol. 36, No. 4, 33, pages: 1050, May 1992 (conference)

abstract [BibTex]

Combining intensity and motion for incremental segmentation and tracking over long image sequences

Black, M. J.

In Proc. Second European Conf. on Computer Vision, ECCV-92, pages: 485-493, LNCS 588, Springer Verlag, May 1992 (inproceedings)

pdf video abstract [BibTex]

Black, M. J. Combining intensity and motion for incremental segmentation and tracking over long image sequences In Proc. Second European Conf. on Computer Vision, ECCV-92, pages: 485-493, LNCS 588, Springer Verlag, May 1992 (inproceedings)

pdf video abstract [BibTex]

Robust Incremental Optical Flow

Black, M. J.

Yale University, Department of Computer Science, New Haven, CT, 1992, Research Report YALEU-DCS-RR-923 (phdthesis)

pdf code [BibTex]

Black, M. J. Robust Incremental Optical Flow Yale University, Department of Computer Science, New Haven, CT, 1992, Research Report YALEU-DCS-RR-923 (phdthesis)

pdf code [BibTex]

1991

Dynamic motion estimation and feature extraction over long image sequences

Black, M. J., Anandan, P.

In Proc. IJCAI Workshop on Dynamic Scene Understanding, Sydney, Australia, August 1991 (inproceedings)

[BibTex]

1991

Black, M. J., Anandan, P. Dynamic motion estimation and feature extraction over long image sequences In Proc. IJCAI Workshop on Dynamic Scene Understanding, Sydney, Australia, August 1991 (inproceedings)

[BibTex]

Robust dynamic motion estimation over time

(IEEE Computer Society Outstanding Paper Award)

Black, M. J., Anandan, P.

In Proc. Computer Vision and Pattern Recognition, CVPR-91,, pages: 296-302, Maui, Hawaii, June 1991 (inproceedings)

Abstract

This paper presents a novel approach to incrementally estimating visual motion over a sequence of images. We start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the formulation of the minimization problem. The resulting objective function is non-convex. Traditional stochastic relaxation techniques for minimizing such functions prove inappropriate for the task. We present a highly parallel incremental stochastic minimization algorithm which has a number of advantages over previous approaches. The incremental nature of the scheme makes it truly dynamic and permits the detection of occlusion and disocclusion boundaries.

pdf video abstract [BibTex]

Black, M. J., Anandan, P. Robust dynamic motion estimation over time In Proc. Computer Vision and Pattern Recognition, CVPR-91,, pages: 296-302, Maui, Hawaii, June 1991 (inproceedings)

pdf video abstract [BibTex]

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