  Human motion transfer aims to transfer motions from a target dynamic person
to a source static one for motion synthesis. An accurate matching between the
source person and the target motion in both large and subtle motion changes is
vital for improving the transferred motion quality. In this paper, we propose
Human MotionFormer, a hierarchical ViT framework that leverages global and
local perceptions to capture large and subtle motion matching, respectively. It
consists of two ViT encoders to extract input features (i.e., a target motion
image and a source human image) and a ViT decoder with several cascaded blocks
for feature matching and motion transfer. In each block, we set the target
motion feature as Query and the source person as Key and Value, calculating the
cross-attention maps to conduct a global feature matching. Further, we
introduce a convolutional layer to improve the local perception after the
global cross-attention computations. This matching process is implemented in
both warping and generation branches to guide the motion transfer. During
training, we propose a mutual learning loss to enable the co-supervision
between warping and generation branches for better motion representations.
Experiments show that our Human MotionFormer sets the new state-of-the-art
performance both qualitatively and quantitatively. Project page:
\url{https://github.com/KumapowerLIU/Human-MotionFormer}
