ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose
  Prompting
Hongwei Zheng Han Li Bowen Shi Wenrui Dai Botao Wan Yu Sun Min Guo Hongkai Xiong
Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency
across sequences to alleviate the depth ambiguity problem but ignore the action
related prior knowledge hidden in the pose sequence. In this paper, we propose
a plug-and-play module named Action Prompt Module (APM) that effectively mines
different kinds of action clues for 3D HPE. The highlight is that, the mining
scheme of APM can be widely adapted to different frameworks and bring
consistent benefits. Specifically, we first present a novel Action-related Text
Prompt module (ATP) that directly embeds action labels and transfers the rich
language information in the label to the pose sequence. Besides, we further
introduce Action-specific Pose Prompt module (APP) to mine the position-aware
pose pattern of each action, and exploit the correlation between the mined
patterns and input pose sequence for further pose refinement. Experiments show
that APM can improve the performance of most video-based 2D-to-3D HPE
frameworks by a large margin.