: "Do as I Do" motion transfer: given a YouTube clip of a ballerina (top), and a video of a graduate student performing various motions, our method transfers the ballerina's performance onto the student (bottom). Video: https://youtu.be/mSaIrz8lM1U Abstract 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-tovideo 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 firstof-its-kind open-source dataset of videos that can be legally used for training and motion transfer. * C. Chan is currently a graduate student at MIT CSAIL. † T. Zhou is currently affiliated with Humen, Inc.
Chan et al. REF proposed a simple but effective method to obtain temporal coherent video results.
Everybody Dance Now
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science