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Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL

Author

Listed:
  • Liang An

    (Tsinghua University)

  • Jilong Ren

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Tao Yu

    (Tsinghua University
    Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist))

  • Tang Hai

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Yichang Jia

    (Tsinghua University
    IDG/McGovern Institute for Brain Research at Tsinghua
    Tsinghua Laboratory of Brain and Intelligence)

  • Yebin Liu

    (Tsinghua University
    Tsinghua University)

Abstract

Understandings of the three-dimensional social behaviors of freely moving large-size mammals are valuable for both agriculture and life science, yet challenging due to occlusions in close interactions. Although existing animal pose estimation methods captured keypoint trajectories, they ignored deformable surfaces which contained geometric information essential for social interaction prediction and for dealing with the occlusions. In this study, we develop a Multi-Animal Mesh Model Alignment (MAMMAL) system based on an articulated surface mesh model. Our self-designed MAMMAL algorithms automatically enable us to align multi-view images into our mesh model and to capture 3D surface motions of multiple animals, which display better performance upon severe occlusions compared to traditional triangulation and allow complex social analysis. By utilizing MAMMAL, we are able to quantitatively analyze the locomotion, postures, animal-scene interactions, social interactions, as well as detailed tail motions of pigs. Furthermore, experiments on mouse and Beagle dogs demonstrate the generalizability of MAMMAL across different environments and mammal species.

Suggested Citation

  • Liang An & Jilong Ren & Tao Yu & Tang Hai & Yichang Jia & Yebin Liu, 2023. "Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43483-w
    DOI: 10.1038/s41467-023-43483-w
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    References listed on IDEAS

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    1. Praneet C. Bala & Benjamin R. Eisenreich & Seng Bum Michael Yoo & Benjamin Y. Hayden & Hyun Soo Park & Jan Zimmermann, 2020. "Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    2. Kang Huang & Yaning Han & Ke Chen & Hongli Pan & Gaoyang Zhao & Wenling Yi & Xiaoxi Li & Siyuan Liu & Pengfei Wei & Liping Wang, 2021. "A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
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