Everybody Dance Now

Caroline Chan, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 5933-5942


This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer.

Related Material

[pdf] [supp]
author = {Chan, Caroline and Ginosar, Shiry and Zhou, Tinghui and Efros, Alexei A.},
title = {Everybody Dance Now},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}