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Existing methods of multi-person video 3D human Pose and Shape Estimation(PSE) typically adopt a two-stage strategy, which first detects human instancesin 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 Shapeestimation framework with progressive Video Transformer, termed PSVT. In PSVT,a spatio-temporal encoder (STE) captures the global feature dependencies amongspatial 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 ofobjects as time proceeds, a novel scheme of progressive decoding is used toupdate pose and shape queries at each frame. Besides, we propose a novelpose-guided attention (PGA) for shape decoder to better predict shapeparameters. The two components strengthen the decoder of PSVT to improveperformance. Extensive experiments on the four datasets show that PSVT achievesstage-of-the-art results.

PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers