Multi-person Physics-based Pose Estimation for Combat Sports
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Abstract (arXiv)
We propose a novel framework for accurate 3D human pose estimation in combat sports using sparse multi-camera setups. Our method integrates robust multi-view 2D pose tracking via a transformer-based top-down approach, employing epipolar geometry constraints and long-term video object segmentation for consistent identity tracking across views. Initial 3D poses are obtained through weighted triangulation and spline smoothing, followed by kinematic optimization to refine pose accuracy. We further enhance pose realism and robustness by introducing a multi-person physics-based trajectory optimization step, effectively addressing challenges such as rapid motions, occlusions, and close interactions. Experimental results on diverse datasets, including a new benchmark of elite boxing footage, demonstrate state-of-the-art performance. Additionally, we release comprehensive annotated video datasets to advance future research in multi-person pose estimation for combat sports.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 39 of its 72 references that are in the map. First posted on arXiv 11 Apr 2025.
Builds on
39 of 72 references.
References · 39
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- 2020Contact and Human Dynamics from Monocular Videocited by 95
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- 2023Hi4D: 4D Instance Segmentation of Close Human Interactioncited by 87
- 2021MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actionscited by 84
- 2019Fast and Robust Multi-Person 3D Pose Estimation from Multiple Viewscited by 83
- 2022ViTPose++: Vision Transformer for Generic Body Pose Estimationcited by 65
- 2021Direct Multi-view Multi-person 3D Pose Estimationcited by 64
- 2022Predictive Sampling: Real-time Behaviour Synthesis with MuJoCocited by 47
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- 2024Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectorycited by 5
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Citing papers per year: 2026: 1.
1 paper.
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