Exploiting Temporal Context for 3D Human Pose Estimation in the Wild

Anurag Arnab, Carl Doersch, Andrew Zisserman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3395-3404

Abstract


We present a bundle-adjustment-based algorithm for recovering accurate 3D human pose and meshes from monocular videos. Unlike previous algorithms which operate on single frames, we show that reconstructing a person over an entire sequence gives extra constraints that can resolve ambiguities. This is because videos often give multiple views of a person, yet the overall body shape does not change and 3D positions vary slowly. Our method improves not only on standard mocap-based datasets like Human 3.6M -- where we show quantitative improvements -- but also on challenging in-the-wild datasets such as Kinetics. Building upon our algorithm, we present a new dataset of more than 3 million frames of YouTube videos from Kinetics with automatically generated 3D poses and meshes. We show that retraining a single-frame 3D pose estimator on this data improves accuracy on both real-world and mocap data by evaluating on the 3DPW and HumanEVA datasets.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Arnab_2019_CVPR,
author = {Arnab, Anurag and Doersch, Carl and Zisserman, Andrew},
title = {Exploiting Temporal Context for 3D Human Pose Estimation in the Wild},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}