  Human Video Motion Transfer (HVMT) aims to, given an image of a source
person, generate his/her video that imitates the motion of the driving person.
Existing methods for HVMT mainly exploit Generative Adversarial Networks (GANs)
to perform the warping operation based on the flow estimated from the source
person image and each driving video frame. However, these methods always
generate obvious artifacts due to the dramatic differences in poses, scales,
and shifts between the source person and the driving person. To overcome these
challenges, this paper presents a novel REgionto-whole human MOtion Transfer
(REMOT) framework based on GANs. To generate realistic motions, the REMOT
adopts a progressive generation paradigm: it first generates each body part in
the driving pose without flow-based warping, then composites all parts into a
complete person of the driving motion. Moreover, to preserve the natural global
appearance, we design a Global Alignment Module to align the scale and position
of the source person with those of the driving person based on their layouts.
Furthermore, we propose a Texture Alignment Module to keep each part of the
person aligned according to the similarity of the texture. Finally, through
extensive quantitative and qualitative experiments, our REMOT achieves
state-of-the-art results on two public benchmarks.
