  Transferring human motion from a source to a target person poses great
potential in computer vision and graphics applications. A crucial step is to
manipulate sequential future motion while retaining the appearance
characteristic.Previous work has either relied on crafted 3D human models or
trained a separate model specifically for each target person, which is not
scalable in practice.This work studies a more general setting, in which we aim
to learn a single model to parsimoniously transfer motion from a source video
to any target person given only one image of the person, named as Collaborative
Parsing-Flow Network (CPF-Net). The paucity of information regarding the target
person makes the task particularly challenging to faithfully preserve the
appearance in varying designated poses. To address this issue, CPF-Net
integrates the structured human parsing and appearance flow to guide the
realistic foreground synthesis which is merged into the background by a
spatio-temporal fusion module. In particular, CPF-Net decouples the problem
into stages of human parsing sequence generation, foreground sequence
generation and final video generation. The human parsing generation stage
captures both the pose and the body structure of the target. The appearance
flow is beneficial to keep details in synthesized frames. The integration of
human parsing and appearance flow effectively guides the generation of video
frames with realistic appearance. Finally, the dedicated designed fusion
network ensure the temporal coherence. We further collect a large set of human
dancing videos to push forward this research field. Both quantitative and
qualitative results show our method substantially improves over previous
approaches and is able to generate appealing and photo-realistic target videos
given any input person image. All source code and dataset will be released at
https://github.com/xiezhy6/CPF-Net.
