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Synthesizing dynamic appearances of humans in motion plays a central role inapplications such as AR/VR and video editing. While many recent methods havebeen proposed to tackle this problem, handling loose garments with complextextures and high dynamic motion still remains challenging. In this paper, wepropose a video based appearance synthesis method that tackles such challengesand demonstrates high quality results for in-the-wild videos that have not beenshown before. Specifically, we adopt a StyleGAN based architecture to the taskof person specific video based motion retargeting. We introduce a novel motionsignature that is used to modulate the generator weights to capture dynamicappearance changes as well as regularizing the single frame based poseestimates to improve temporal coherency. We evaluate our method on a set ofchallenging videos and show that our approach achieves state-of-the artperformance both qualitatively and quantitatively.

Dance In the Wild: Monocular Human Animation with Neural Dynamic Appearance Synthesis