Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints



Abstract
In this paper, we aim to reduce the footskate artifacts when reconstructing human dynamics from monocular RGB videos. Recent work has made substantial progress in improving the temporal smoothness of the reconstructed motion trajectories. Their results, however, still suffer from severe foot skating and slippage artifacts. To tackle this issue, we present a neural network based detector for localizing ground contact events of human feet and use it to impose a physical constraint for optimization of the whole human dynamics in a video. We present a detailed study on the proposed ground contact detector and demonstrate high-quality human motion reconstruction results in various videos.

Papers

WACV2020
Supplementary Material
Citation

Yuliang Zou, Jimei Yang, Duygu Ceylan, Jianming Zhang, Federico Perazzi, and Jia-Bin Huang, "Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints", In Proceedings of Winter Conference on Applications of Computer Vision, 2020.


Bibtex
@inproceedings{zou2020reducing,
    author    = {Zou, Yuliang and Yang, Jimei and Ceylan, Duygu and Zhang, Jianming and Perazzi, Federico and Huang, Jia-Bin}, 
    title     = {Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints}, 
    booktitle = {Winter Conference on Applications of Computer Vision},
    year      = {2020}
}
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Network Architecture

Temporal Convolutional Network for Ground Contact Prediction
Results
Ground Contact Prediction
Motion Reconstruction
References