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

16 Mar 2022  ·  Ailing Zeng, Xuan Ju, Lei Yang, Ruiyuan Gao, Xizhou Zhu, Bo Dai, Qiang Xu ·

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. Code is available at

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Human Pose Estimation 3DPW DeciWatch-PARE PA-MPJPE 46.4 # 17
MPJPE 75.5 # 17
3D Human Pose Estimation AIST++ DeciWatch MPJPE 67.2 # 2
Single-view Y # 1
3D Human Pose Estimation Human3.6M DeciWatch Average MPJPE (mm) 53.1 # 172
Pose Estimation J-HMDB DeciWatch Mean PCK@0.2 99.0 # 1
Mean PCK@0.1 94.6 # 1
Mean PCK@0.05 80.6 # 1
2D Human Pose Estimation JHMDB (2D poses only) DeciWatch PCK 98.8 # 1