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

Liang An, Jilong Ren, Tao Yu, Tang Hai (), Yichang Jia () and Yebin Liu ()
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Liang An: Tsinghua University
Jilong Ren: Chinese Academy of Sciences
Tao Yu: Tsinghua University
Tang Hai: Chinese Academy of Sciences
Yichang Jia: Tsinghua University
Yebin Liu: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43483-w

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