Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos

Authors: J. Song, L. Wang, L. Van Gool, O. Hilliges,
publication: In Proceedings CVPR, Hawaii, USA, 2017


In summary our main contributions are: (A). a structured model captures the inherent consistency of human poses in video sequences based on a loopy spatio-temporal graph. (B). An efficient and flexible infer- ence layer performs message passing along the spatial and temporal graph edges and significantly reduces joint posi- tion uncertainty (C). The entire architecture integrates a ConvNet-based joint regressors and a high-level structured inference model in a unified framework which can be op- timized in an end-to-end manner. Code and models will be available at GitHub.


Deep ConvNets have been shown to be effective for the task of human pose estimation from single images. However, several challenging issues arise in the video-based case such as self-occlusion, motion blur, and uncommon poses with few or no examples in the training data. Temporal information can provide additional cues about the location of body joints and help to alleviate these issues. In this paper, we propose a deep structured model to estimate a sequence of human poses in unconstrained videos. This model can be efficiently trained in an end-to-end manner and is capable of representing the appearance of body joints and their spatio-temporal relationships simultaneously. Domain knowledge about the human body is explicitly incorporated into the network providing effective priors to regularize the skeletal structure and to enforce temporal consistency. The proposed end-to-end architecture is evaluated on two widely used benchmarks for video-based pose estimation (Penn Action and JHMDB datasets). Our approach outperforms several state-of-the-art methods.



	author = {Song, Jie and Wang, Limin and Van Gool, Luc and Hilliges, Otmar},
	title = {Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos},
	booktitle = {CVPR},
	year = {2017},
	location = {Hawaii, USA},