DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation

• URL: http://arxiv.org/abs/2203.08713v1
• Date: Wed, 16 Mar 2022 16:03:37 GMT
• Title: DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
• Authors: Ailing Zeng, Xuan Ju, Lei Yang, Ruiyuan Gao, Xizhou Zhu, Bo Dai, Qiang Xu
• Abstract summary: DeciWatch is a framework for video-based 2D/3D human pose estimation. It can achieve 10 times efficiency improvement over existing works without any performance degradation.
• Score: 58.06505882939195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation. Specifically, DeciWatch uniformly samples less than 10% video frames for detailed estimation, denoises the estimated 2D/3D poses with an efficient Transformer architecture, and then accurately recovers the rest of the frames using another Transformer-based network. Comprehensive experimental results on three video-based human pose estimation and body mesh recovery tasks with four datasets validate the efficiency and effectiveness of DeciWatch.

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