Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation

arXiv:2311.12028v2 Announce Type: replace-cross
Abstract: Transformers have been successfully applied in the field of video-based 3D human pose estimation. However, the high computational costs of these video pose transformers (VPTs) make them impractical on resource-constrained devices. In this paper, we present a plug-and-play pruning-and-recovering framework, called Hourglass Tokenizer (HoT), for efficient transformer-based 3D human pose estimation from videos. Our HoT begins with pruning pose tokens of redundant frames and ends with recovering full-length tokens, resulting in a few pose tokens …

arxiv cs.ai cs.cv cs.lg human transformer type