  Existing methods of multi-person video 3D human Pose and Shape Estimation
(PSE) typically adopt a two-stage strategy, which first detects human instances
in each frame and then performs single-person PSE with temporal model. However,
the global spatio-temporal context among spatial instances can not be captured.
In this paper, we propose a new end-to-end multi-person 3D Pose and Shape
estimation framework with progressive Video Transformer, termed PSVT. In PSVT,
a spatio-temporal encoder (STE) captures the global feature dependencies among
spatial objects. Then, spatio-temporal pose decoder (STPD) and shape decoder
(STSD) capture the global dependencies between pose queries and feature tokens,
shape queries and feature tokens, respectively. To handle the variances of
objects as time proceeds, a novel scheme of progressive decoding is used to
update pose and shape queries at each frame. Besides, we propose a novel
pose-guided attention (PGA) for shape decoder to better predict shape
parameters. The two components strengthen the decoder of PSVT to improve
performance. Extensive experiments on the four datasets show that PSVT achieves
stage-of-the-art results.
