In this paper, we study the problem of end-to-end multi-person pose
estimation. State-of-the-art solutions adopt the DETR-like framework, and
mainly develop the complex decoder, e.g., regarding pose estimation as keypoint
box detection and combining with human detection in ED-Pose, hierarchically
predicting with pose decoder and joint (keypoint) decoder in PETR. We present a
simple yet effective transformer approach, named Group Pose. We simply regard
$K$-keypoint pose estimation as predicting a set of $N\times K$ keypoint
positions, each from a keypoint query, as well as representing each pose with
an instance query for scoring $N$ pose predictions. Motivated by the intuition
that the interaction, among across-instance queries of different types, is not
directly helpful, we make a simple modification to decoder self-attention. We
replace single self-attention over all the $N\times(K+1)$ queries with two
subsequent group self-attentions: (i) $N$ within-instance self-attention, with
each over $K$ keypoint queries and one instance query, and (ii) $(K+1)$
same-type across-instance self-attention, each over $N$ queries of the same
type. The resulting decoder removes the interaction among across-instance
type-different queries, easing the optimization and thus improving the
performance. Experimental results on MS COCO and CrowdPose show that our
approach without human box supervision is superior to previous methods with
complex decoders, and even is slightly better than ED-Pose that uses human box
supervision. $\href{this https URL}{\rm
Paddle}$ and $\href{this https URL}{\rm PyTorch}$ code
are available.

通过简单且有效的 Transformer 方法，命名为 Group Pose，我们研究了无人工盒监督的端到端多人姿态估计问题，取得了优于复杂解码器的性能。