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VoxelPose: Towards Multi-camera 3D Human Pose Estimation in Wild Environment

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

We present VoxelPose to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimates, VoxelPose directly operates in the 3D space therefore avoids making incorrect decisions in each camera view. To achieve this goal, features in all camera views are aggregated in the 3D voxel space and fed into Cuboid Proposal Network (CPN) to localize all people. Then we propose Pose Regression Network (PRN) to estimate a detailed 3D pose for each proposal. The approach is robust to occlusion which occurs frequently in practice. Without bells and whistles, it outperforms the previous methods on several public datasets.

This work is done when Hanyue Tu is an intern at Microsoft Research Asia.

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Notes

  1. 1.

    https://youtu.be/qZAyHUzdpgw.

  2. 2.

    https://youtu.be/AgDQFIlL5IM.

  3. 3.

    https://youtu.be/S6G3TXaBukw.

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Authors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Hanyue Tu, Chunyu Wang & Wenjun Zeng

  2. University of Science and Technology of China, Hefei, China

    Hanyue Tu

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  1. Hanyue Tu
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  2. Chunyu Wang
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  3. Wenjun Zeng
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Corresponding author

Correspondence to Chunyu Wang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Tu, H., Wang, C., Zeng, W. (2020). VoxelPose: Towards Multi-camera 3D Human Pose Estimation in Wild Environment. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12346. Springer, Cham. https://doi.org/10.1007/978-3-030-58452-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-58452-8_12

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