Cordelia Schmid

INRIA Research Director (visits: Jan. 2013, May 2014, Jan. 2015, April 2016, April/May 2017)

Perceiving Systems

Office: N3.003
Max-Planck-Ring 4
72076 Tübingen
Germany

http://lear.inrialpes.fr/~schmid/

Publications

3 results (BibTeX)

2017

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 [BibTex]

2017

Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., Schmid, C. Learning from Synthetic Humans In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

arXiv project data [BibTex]

2013

Towards understanding action recognition

Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3192-3199, IEEE, Sydney, Australia, December 2013 (inproceedings)

Abstract

Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important – for example, should we work on improving flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that highlevel pose features greatly outperform low/mid level features; in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features; this suggests it is important to improve optical flow and human detection algorithms. Our analysis and JHMDB dataset should facilitate a deeper understanding of action recognition algorithms.

Website Errata Poster Paper Slides DOI Project Page [BibTex]

2013

Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J. Towards understanding action recognition In IEEE International Conference on Computer Vision (ICCV), pages: 3192-3199, IEEE, Sydney, Australia, December 2013 (inproceedings)

Website Errata Poster Paper Slides DOI Project Page [BibTex]

Estimating Human Pose with Flowing Puppets

Zuffi, S., Romero, J., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3312-3319, 2013 (inproceedings)

Abstract

We address the problem of upper-body human pose estimation in uncontrolled monocular video sequences, without manual initialization. Most current methods focus on isolated video frames and often fail to correctly localize arms and hands. Inferring pose over a video sequence is advantageous because poses of people in adjacent frames exhibit properties of smooth variation due to the nature of human and camera motion. To exploit this, previous methods have used prior knowledge about distinctive actions or generic temporal priors combined with static image likelihoods to track people in motion. Here we take a different approach based on a simple observation: Information about how a person moves from frame to frame is present in the optical flow field. We develop an approach for tracking articulated motions that "links" articulated shape models of people in adjacent frames trough the dense optical flow. Key to this approach is a 2D shape model of the body that we use to compute how the body moves over time. The resulting "flowing puppets" provide a way of integrating image evidence across frames to improve pose inference. We apply our method on a challenging dataset of TV video sequences and show state-of-the-art performance.

pdf code data DOI Project Page Project Page [BibTex]

Zuffi, S., Romero, J., Schmid, C., Black, M. J. Estimating Human Pose with Flowing Puppets In IEEE International Conference on Computer Vision (ICCV), pages: 3312-3319, 2013 (inproceedings)

pdf code data DOI Project Page Project Page [BibTex]

Cordelia Schmid

2017

Learning from Synthetic Humans

2017

2013

Towards understanding action recognition

2013

Estimating Human Pose with Flowing Puppets

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