  Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh
recovery) involves the human body, hand, and expression estimation. Most
existing methods have tackled this task in a two-stage manner, first detecting
the human body part with an off-the-shelf detection model and inferring the
different human body parts individually. Despite the impressive results
achieved, these methods suffer from 1) loss of valuable contextual information
via cropping, 2) introducing distractions, and 3) lacking inter-association
among different persons and body parts, inevitably causing performance
degradation, especially for crowded scenes. To address these issues, we
introduce a novel all-in-one-stage framework, AiOS, for multiple expressive
human pose and shape recovery without an additional human detection step.
Specifically, our method is built upon DETR, which treats multi-person
whole-body mesh recovery task as a progressive set prediction problem with
various sequential detection. We devise the decoder tokens and extend them to
our task. Specifically, we first employ a human token to probe a human location
in the image and encode global features for each instance, which provides a
coarse location for the later transformer block. Then, we introduce a
joint-related token to probe the human joint in the image and encoder a
fine-grained local feature, which collaborates with the global feature to
regress the whole-body mesh. This straightforward but effective model
outperforms previous state-of-the-art methods by a 9% reduction in NMVE on
AGORA, a 30% reduction in PVE on EHF, a 10% reduction in PVE on ARCTIC, and a
3% reduction in PVE on EgoBody.
