  Human video motion transfer (HVMT) aims to synthesize videos that one person
imitates other persons' actions. Although existing GAN-based HVMT methods have
achieved great success, they either fail to preserve appearance details due to
the loss of spatial consistency between synthesized and exemplary images, or
generate incoherent video results due to the lack of temporal consistency among
video frames. In this paper, we propose Coarse-to-Fine Flow Warping Network
(C2F-FWN) for spatial-temporal consistent HVMT. Particularly, C2F-FWN utilizes
coarse-to-fine flow warping and Layout-Constrained Deformable Convolution
(LC-DConv) to improve spatial consistency, and employs Flow Temporal
Consistency (FTC) Loss to enhance temporal consistency. In addition, provided
with multi-source appearance inputs, C2F-FWN can support appearance attribute
editing with great flexibility and efficiency. Besides public datasets, we also
collected a large-scale HVMT dataset named SoloDance for evaluation. Extensive
experiments conducted on our SoloDance dataset and the iPER dataset show that
our approach outperforms state-of-art HVMT methods in terms of both spatial and
temporal consistency. Source code and the SoloDance dataset are available at
https://github.com/wswdx/C2F-FWN.
