hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
Ci Li, Nima Ghorbani, Sofia Broomé, Maheen Rashid, Michael J. Black, Elin Hernlund, Hedvig Kjellström, Silvia Zuffi
To appear in CV4Animals Workshop, Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
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Abstract
In this paper we present our preliminary work on model-based behavioral analysis of horse motion. Our approach is based on the SMAL model [21], a 3D articulated statistical model of animal shape. We define a novel SMAL model for horses based on a new template, skeleton and shape space learned from 37 horse toys. We test the accuracy of our hSMAL model in reconstructing a horse from 3D mocap data and images. We apply the hSMAL model to the problem of lameness detection from video, where we fit the model to images to recover 3D pose and train an ST-GCN network [17] on pose data. A comparison with the same network trained on mocap points illustrates the benefit of our approach.