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Pose for Everything: Towards Category-Agnostic Pose Estimation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Existing works on 2D pose estimation mainly focus on a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting the poses/keypoints of the unseen class of objects. In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition. To achieve this goal, we formulate the pose estimation problem as a keypoint matching problem and design a novel CAPE framework, termed POse Matching Network (POMNet). A transformer-based Keypoint Interaction Module (KIM) is proposed to capture both the interactions among different keypoints and the relationship between the support and query images. We also introduce Multi-category Pose (MP-100) dataset, which is a 2D pose dataset of 100 object categories containing over 20K instances and is well-designed for developing CAPE algorithms. Experiments show that our method outperforms other baseline approaches by a large margin. Codes and data are available at https://github.com/luminxu/Pose-for-Everything.

L. Xu and S. Jin—Equal contribution.

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Acknowledgement

This work is supported in part by the General Research Fund through the Research Grants Council of Hong Kong under Grants (Nos. 14202217, 14203118, 14208619), in part by Research Impact Fund Grant No. R5001-18. Ping Luo is supported by the General Research Fund of HK No. 27208720, No. 17212120, and No. 17200622. Wanli Ouyang is supported by the Australian Research Council Grant DP200103223, Australian Medical Research Future Fund MRFAI000085, CRC-P Smart Material Recovery Facility (SMRF) - Curby Soft Plastics, and CRC-P ARIA - Bionic Visual-Spatial Prosthesis for the Blind.

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Authors and Affiliations

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

    Lumin Xu, Wang Zeng & Xiaogang Wang

  2. SenseTime Research, Beijing, China

    Lumin Xu & Wang Zeng

  3. The University of Hong Kong, Pok Fu Lam, Hong Kong

    Sheng Jin & Ping Luo

  4. SenseTime Research and Tetras.AI, Beijing, China

    Sheng Jin, Wentao Liu & Chen Qian

  5. Shanghai AI Laboratory, Shanghai, China

    Wentao Liu & Wanli Ouyang

  6. The University of Sydney, Sydney, Australia

    Wanli Ouyang

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  1. Lumin Xu
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  8. Xiaogang Wang
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Correspondence to Lumin 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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Xu, L. et al. (2022). Pose for Everything: Towards Category-Agnostic 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 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_23

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