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DeciWatch: A Simple Baseline for \(10\times \) Efficient 2D and 3D Pose Estimation

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Computer Vision – ECCV 2022 (ECCV 2022)

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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, body mesh recovery tasks and efficient labeling in videos with four datasets validate the efficiency and effectiveness of DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch.

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Notes

  1. 1.

    Due to the limit of pages, we present data description, comprehensive results of different sampling ratios, the effect of hyper-parameters, generalization ability, qualitative results, and failure cases analyses in the supplementary material.

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Acknowledgement

This work is supported in part by Shenzhen-Hong Kong-Macau Science and Technology Program (Category C) of Shenzhen Science Technology and Innovation Commission under Grant No. SGDX2020110309500101, and Shanghai AI Laboratory.

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Hong Kong, China

    Ailing Zeng, Xuan Ju, Ruiyuan Gao & Qiang Xu

  2. Sensetime Group Limited, Hong Kong, China

    Lei Yang & Xizhou Zhu

  3. Shanghai AI Laboratory, Shanghai, China

    Bo Dai

Authors
  1. Ailing Zeng
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  2. Xuan Ju
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  3. Lei Yang
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  4. Ruiyuan Gao
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  5. Xizhou Zhu
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  6. Bo Dai
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  7. Qiang Xu
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Corresponding author

Correspondence to Qiang Xu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zeng, A. et al. (2022). DeciWatch: A Simple Baseline for \(10\times \) Efficient 2D and 3D Pose Estimation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_35

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