Unpaired Motion Style Transfer with Motion-Oriented Projection Flow Network

Yue Huang, Haoran Mo, Xiao Liang, Chengying Gao

Abstract: Existing motion style transfer methods trained with unpaired samples tend to generate motions with inconsistent content or inconsistent number of frames when compared with the source motion. Moreover, due to the limited training samples, these methods perform worse in unseen style. In this paper, we propose a novel unpaired motion style transfer framework that generates complete stylized motions with consistent content. We introduce a motion-oriented projection flow network (M-PFN) designed for temporal motion data, which encodes the content and style motions into latent codes and decodes the stylized features produced by adaptive instance normalization (AdaIN) into stylized motions. The M-PFN contains dedicated operations and modules, e.g., Transformer, to process the temporal information of motions, which help to improve the continuity of the generated motions. Comparisons with the state-of-the-art methods show that our method effectively transfers the style of the motions while retaining the complete content and has stronger generalization ability in unseen style features.