We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person. Without using any paired data for supervision, the proposed method can be trained in an unsupervised manner by exploiting invariance properties of three orthogonal factors of variation including motion, structure, and view-angle. Specifically, with loss functions carefully derived based on invariance, we train an auto-encoder to disentangle the latent representations of such factors given the source and target video clips. This allows us to selectively transfer motion extracted from the source video seamlessly to the target video in spite of structural and view-angle disparities between the source and the target. The relaxed assumption of paired data allows our method to be trained on a vast amount of videos needless of manual annotation of source-target pairing, leading to improved robustness against large structural variations and extreme motion in videos. We demonstrate the effectiveness of our method over the state-of-the-art methods. Code, model and data are publicly available on our project page (https://yzhq97.github.io/transmomo).
@article{arxiv.2003.14401,
title = {TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting},
author = {Zhuoqian Yang and Wentao Zhu and Wayne Wu and Chen Qian and Qiang Zhou and Bolei Zhou and Chen Change Loy},
journal= {arXiv preprint arXiv:2003.14401},
year = {2020}
}
Comments
Accepted by CVPR 2020. The first three authors contributed equally. Project page: https://yzhq97.github.io/transmomo/