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2017


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Learning a model of facial shape and expression from 4D scans

Li, T., Bolkart, T., Black, M. J., Li, H., Romero, J.

ACM Transactions on Graphics, 36(6):194:1-194:17, November 2017, Two first authors contributed equally (article)

Abstract
The field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishable from real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (Faces Learned with an Articulated Model and Expressions) is designed to work with existing graphics software and be easy to fit to data. FLAME uses a linear shape space trained from 3800 scans of human heads. FLAME combines this linear shape space with an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and additional global expression from 4D face sequences in the D3DFACS dataset along with additional 4D sequences.We accurately register a template mesh to the scan sequences and make the D3DFACS registrations available for research purposes. In total the model is trained from over 33, 000 scans. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. We compare FLAME to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. FLAME is significantly more accurate and is available for research purposes (http://flame.is.tue.mpg.de).

data/model video code chumpy code tensorflow paper supplemental Project Page [BibTex]

2017

data/model video code chumpy code tensorflow paper supplemental Project Page [BibTex]


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Investigating Body Image Disturbance in Anorexia Nervosa Using Novel Biometric Figure Rating Scales: A Pilot Study

Mölbert, S. C., Thaler, A., Streuber, S., Black, M. J., Karnath, H., Zipfel, S., Mohler, B., Giel, K. E.

European Eating Disorders Review, 25(6):607-612, November 2017 (article)

Abstract
This study uses novel biometric figure rating scales (FRS) spanning body mass index (BMI) 13.8 to 32.2 kg/m2 and BMI 18 to 42 kg/m2. The aims of the study were (i) to compare FRS body weight dissatisfaction and perceptual distortion of women with anorexia nervosa (AN) to a community sample; (ii) how FRS parameters are associated with questionnaire body dissatisfaction, eating disorder symptoms and appearance comparison habits; and (iii) whether the weight spectrum of the FRS matters. Women with AN (n = 24) and a community sample of women (n = 104) selected their current and ideal body on the FRS and completed additional questionnaires. Women with AN accurately picked the body that aligned best with their actual weight in both FRS. Controls underestimated their BMI in the FRS 14–32 and were accurate in the FRS 18–42. In both FRS, women with AN desired a body close to their actual BMI and controls desired a thinner body. Our observations suggest that body image disturbance in AN is unlikely to be characterized by a visual perceptual disturbance, but rather by an idealization of underweight in conjunction with high body dissatisfaction. The weight spectrum of FRS can influence the accuracy of BMI estimation.

publisher DOI Project Page [BibTex]


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Embodied Hands: Modeling and Capturing Hands and Bodies Together

Romero, J., Tzionas, D., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):245:1-245:17, 245:1–245:17, ACM, November 2017 (article)

Abstract
Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.

website youtube paper suppl video link (url) DOI Project Page [BibTex]

website youtube paper suppl video link (url) DOI Project Page [BibTex]


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A Generative Model of People in Clothing

Lassner, C., Pons-Moll, G., Gehler, P. V.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, October 2017 (inproceedings)

Abstract
We present the first image-based generative model of people in clothing in a full-body setting. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible.

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Semantic Video CNNs through Representation Warping

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

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, October 2017 (inproceedings) Accepted

Abstract
In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to augment existing architectures with very lit- tle extra computational cost. This module is called Net- Warp and we demonstrate its use for a range of network architectures. The main design principle is to use optical flow of adjacent frames for warping internal network repre- sentations across time. A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to- end training. Experiments validate that the proposed ap- proach incurs only little extra computational cost, while im- proving performance, when video streams are available. We achieve new state-of-the-art results on the standard CamVid and Cityscapes benchmark datasets and show reliable im- provements over different baseline networks. Our code and models are available at http://segmentation.is. tue.mpg.de

pdf Supplementary Project Page [BibTex]

pdf Supplementary Project Page [BibTex]


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A simple yet effective baseline for 3d human pose estimation

Martinez, J., Hossain, R., Romero, J., Little, J. J.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, October 2017 (inproceedings)

Abstract
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3-dimensional positions. With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feed-forward network outperforms the best reported result by about 30\% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (\ie, using images as input) yields state of the art results -- this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.

video code arxiv pdf preprint Project Page [BibTex]

video code arxiv pdf preprint Project Page [BibTex]


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An Online Scalable Approach to Unified Multirobot Cooperative Localization and Object Tracking

Ahmad, A., Lawless, G., Lima, P.

IEEE Transactions on Robotics (T-RO), 33, pages: 1184 - 1199, October 2017 (article)

Abstract
In this article we present a unified approach for multi-robot cooperative simultaneous localization and object tracking based on particle filters. Our approach is scalable with respect to the number of robots in the team. We introduce a method that reduces, from an exponential to a linear growth, the space and computation time requirements with respect to the number of robots in order to maintain a given level of accuracy in the full state estimation. Our method requires no increase in the number of particles with respect to the number of robots. However, in our method each particle represents a full state hypothesis, leading to the linear dependency on the number of robots of both space and time complexity. The derivation of the algorithm implementing our approach from a standard particle filter algorithm and its complexity analysis are presented. Through an extensive set of simulation experiments on a large number of randomized datasets, we demonstrate the correctness and efficacy of our approach. Through real robot experiments on a standardized open dataset of a team of four soccer playing robots tracking a ball, we evaluate our method's estimation accuracy with respect to the ground truth values. Through comparisons with other methods based on i) nonlinear least squares minimization and ii) joint extended Kalman filter, we further highlight our method's advantages. Finally, we also present a robustness test for our approach by evaluating it under scenarios of communication and vision failure in teammate robots.

Published Version link (url) DOI [BibTex]


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Effects of animation retargeting on perceived action outcomes

Kenny, S., Mahmood, N., Honda, C., Black, M. J., Troje, N. F.

Proceedings of the ACM Symposium on Applied Perception (SAP’17), pages: 2:1-2:7, September 2017 (conference)

Abstract
The individual shape of the human body, including the geometry of its articulated structure and the distribution of weight over that structure, influences the kinematics of a person's movements. How sensitive is the visual system to inconsistencies between shape and motion introduced by retargeting motion from one person onto the shape of another? We used optical motion capture to record five pairs of male performers with large differences in body weight, while they pushed, lifted, and threw objects. Based on a set of 67 markers, we estimated both the kinematics of the actions as well as the performer's individual body shape. To obtain consistent and inconsistent stimuli, we created animated avatars by combining the shape and motion estimates from either a single performer or from different performers. In a virtual reality environment, observers rated the perceived weight or thrown distance of the objects. They were also asked to explicitly discriminate between consistent and hybrid stimuli. Observers were unable to accomplish the latter, but hybridization of shape and motion influenced their judgements of action outcome in systematic ways. Inconsistencies between shape and motion were assimilated into an altered perception of the action outcome.

pdf DOI [BibTex]

pdf DOI [BibTex]


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Coupling Adaptive Batch Sizes with Learning Rates

Balles, L., Romero, J., Hennig, P.

In Proceedings Conference on Uncertainty in Artificial Intelligence (UAI) 2017, pages: 410-419, (Editors: Gal Elidan and Kristian Kersting), Association for Uncertainty in Artificial Intelligence (AUAI), August 2017 (inproceedings)

Abstract
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On three image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.

Code link (url) Project Page [BibTex]

Code link (url) Project Page [BibTex]


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Joint Graph Decomposition and Node Labeling by Local Search

Levinkov, E., Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., Andres, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1904-1912, IEEE, July 2017 (inproceedings)

PDF Supplementary DOI Project Page [BibTex]

PDF Supplementary DOI Project Page [BibTex]


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Dynamic FAUST: Registering Human Bodies in Motion

Bogo, F., Romero, J., Pons-Moll, G., Black, M. J.

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

Abstract
While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data; that is 3D scans of moving nonrigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http://dfaust.is.tue.mpg.de.

pdf video Project Page Project Page Project Page [BibTex]

pdf video Project Page Project Page Project Page [BibTex]


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Learning from Synthetic Humans

Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., Schmid, C.

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

Abstract
Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.

arXiv project data Project Page Project Page [BibTex]

arXiv project data Project Page Project Page [BibTex]


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On human motion prediction using recurrent neural networks

Martinez, J., Black, M. J., Romero, J.

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

Abstract
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state-of-the-art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which result in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.

arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


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Articulated Multi-person Tracking in the Wild

Insafutdinov, E., Andriluka, M., Pishchulin, L., Tang, S., Levinkov, E., Andres, B., Schiele, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1293-1301, IEEE, July 2017, Oral (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

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

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 1406-1416, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.

pdf suppmat Project page Video DOI Project Page [BibTex]

pdf suppmat Project page Video DOI Project Page [BibTex]


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Optical Flow in Mostly Rigid Scenes

Wulff, J., Sevilla-Lara, L., Black, M. J.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 6911-6920, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPISintel and KITTI-2015 benchmarks.

pdf SupMat video code Project Page [BibTex]

pdf SupMat video code Project Page [BibTex]


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OctNet: Learning Deep 3D Representations at High Resolutions

Riegler, G., Ulusoy, O., Geiger, A.

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

Abstract
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.

pdf suppmat Project Page Video Project Page [BibTex]

pdf suppmat Project Page Video Project Page [BibTex]


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Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Nestmeyer, T., Gehler, P. V.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1771-1780, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pre-print DOI Project Page Project Page [BibTex]

pre-print DOI Project Page Project Page [BibTex]


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Detailed, accurate, human shape estimation from clothed 3D scan sequences

Zhang, C., Pujades, S., Black, M., Pons-Moll, G.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Washington, DC, USA, July 2017, Spotlight (inproceedings)

Abstract
We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited statistical models of body shape produce overly smooth shapes lacking personalized details. In this paper we contribute a new approach to recover not only an approximate shape of the person, but also their detailed shape. Our approach allows the estimated shape to deviate from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available a new high quality 4D dataset that enables quantitative evaluation. Our method outperforms the previous state of the art, both qualitatively and quantitatively.

arxiv_preprint video dataset pdf supplemental DOI Project Page [BibTex]

arxiv_preprint video dataset pdf supplemental DOI Project Page [BibTex]


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3D Menagerie: Modeling the 3D Shape and Pose of Animals

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

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 5524-5532, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract
There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we learn our model from a small set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the registrations and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the registration to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fit to data. We demonstrate generalization by fitting it to images of real animals including species not seen in training.

pdf video Project Page [BibTex]

pdf video Project Page [BibTex]


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Optical Flow Estimation using a Spatial Pyramid Network

Ranjan, A., Black, M.

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

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

pdf SupMat project/code [BibTex]

pdf SupMat project/code [BibTex]


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Multiple People Tracking by Lifted Multicut and Person Re-identification

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

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

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Video Propagation Networks

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

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

pdf supplementary arXiv project page code Project Page [BibTex]

pdf supplementary arXiv project page code Project Page [BibTex]


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Generating Descriptions with Grounded and Co-Referenced People

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

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

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels

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

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

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

YouTube pdf suppmat Project Page [BibTex]

YouTube pdf suppmat Project Page [BibTex]


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Deep representation learning for human motion prediction and classification

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

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

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

arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


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

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

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

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

arXiv project/code/data Project Page [BibTex]

arXiv project/code/data Project Page [BibTex]


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Early Stopping Without a Validation Set

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

arXiv preprint arXiv:1703.09580, 2017 (article)

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

link (url) Project Page Project Page [BibTex]


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Data-Driven Physics for Human Soft Tissue Animation

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

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

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

video paper link (url) Project Page [BibTex]

video paper link (url) Project Page [BibTex]


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

(Best Paper, Eurographics 2017)

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

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

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

video pdf Project Page [BibTex]

video pdf Project Page [BibTex]


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

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

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

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

arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


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ClothCap: Seamless 4D Clothing Capture and Retargeting

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

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

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

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

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


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

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

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

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

Code pdf DOI Project Page [BibTex]

2008


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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

pdf Springerlink version [BibTex]


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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]


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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]

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


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Infinite Kernel Learning

Gehler, P., Nowozin, S.

(178), Max Planck Institute, octomber 2008 (techreport)

project page pdf [BibTex]

project page pdf [BibTex]


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A non-parametric Bayesian alternative to spike sorting

Wood, F., Black, M. J.

J. Neuroscience Methods, 173(1):1–12, August 2008 (article)

Abstract
The analysis of extra-cellular neural recordings typically begins with careful spike sorting and all analysis of the data then rests on the correctness of the resulting spike trains. In many situations this is unproblematic as experimental and spike sorting procedures often focus on well isolated units. There is evidence in the literature, however, that errors in spike sorting can occur even with carefully collected and selected data. Additionally, chronically implanted electrodes and arrays with fixed electrodes cannot be easily adjusted to provide well isolated units. In these situations, multiple units may be recorded and the assignment of waveforms to units may be ambiguous. At the same time, analysis of such data may be both scientifically important and clinically relevant. In this paper we address this issue using a novel probabilistic model that accounts for several important sources of uncertainty and error in spike sorting. In lieu of sorting neural data to produce a single best spike train, we estimate a probabilistic model of spike trains given the observed data. We show how such a distribution over spike sortings can support standard neuroscientific questions while providing a representation of uncertainty in the analysis. As a representative illustration of the approach, we analyzed primary motor cortical tuning with respect to hand movement in data recorded with a chronic multi-electrode array in non-human primates.We found that the probabilistic analysis generally agrees with human sorters but suggests the presence of tuned units not detected by humans.

pdf preprint pdf from publisher PubMed [BibTex]

pdf preprint pdf from publisher PubMed [BibTex]


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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]

Pdf [BibTex]


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Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

(J. Neural Engineering Highlights of 2008 Collection)

Kim, S., Simeral, J., Hochberg, L., Donoghue, J. P., Black, M. J.

J. Neural Engineering, 5, pages: 455–476, 2008 (article)

Abstract
Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor’s velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding.

pdf preprint pdf from publisher [BibTex]

pdf preprint pdf from publisher [BibTex]


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Incremental nonparametric Bayesian regression

Wood, F., Grollman, D. H., Heller, K. A., Jenkins, O. C., Black, M. J.

(CS-08-07), Brown University, Department of Computer Science, 2008 (techreport)

pdf [BibTex]

pdf [BibTex]


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Brownian Warps for Non-Rigid Registration

Mads Nielsen, Peter Johansen, Andrew Jackson, Benny Lautrup, Soren Hauberg

Journal of Mathematical Imaging and Vision, 31, pages: 221-231, Springer Netherlands, 2008 (article)

Publishers site PDF [BibTex]

Publishers site PDF [BibTex]


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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]

Pdf [BibTex]


no image
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]

[BibTex]


Thumb xl screen shot 2012 06 06 at 11.28.04 am
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]

project page pdf [BibTex]


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An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application

Soren Hauberg, Jakob Sloth

Journal of Mathematical Imaging and Vision, 31, pages: 165-170, Springer Netherlands, 2008 (article)

Publishers site Paper site PDF [BibTex]

Publishers site Paper site PDF [BibTex]


Thumb xl thumb screen shot 2012 10 06 at 12.29.08 pm
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]

Pdf [BibTex]


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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]

[BibTex]


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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]

[BibTex]


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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]

[BibTex]