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hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition

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arxiv 2106.10102 v1 pith:5N4FVUL4 submitted 2021-06-18 cs.CV

Ci Li , Nima Ghorbani , Sofia Broomé , Maheen Rashid , Michael J. Black , Elin Hernlund , Hedvig Kjellström , Silvia Zuffi This is my paper · ORCID
classification cs.CV
keywords modelhorsehsmalposeshapeapproachdataimages
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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, 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 on pose data. A comparison with the same network trained on mocap points illustrates the benefit of our approach.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CORGI: Consistency-Aware 3D Dog Reconstruction from a Single Image in the Wild

    cs.CV 2026-07 unverdicted novelty 7.0 of 10

    CORGI reconstructs high-fidelity, animatable 3D dogs from a single in-the-wild image via canonical orbital generation, deformable 3DGS anchored to D-SMAL, and self-supervised generative repair, without 3D supervision.

  2. AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A family-aware Transformer with supervised contrastive learning and a diffusion-generated synthetic dataset achieves state-of-the-art 3D animal pose and shape estimation.

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