Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball

• URL: http://arxiv.org/abs/2605.29953v1
• Date: Thu, 28 May 2026 13:59:30 GMT
• Title: Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball
• Abstract summary: Mesh-Aware Epipolar Matching (MAEM) is a training-free framework for multi-view multi-person 3D pose estimation.<n> Experiments on two public multi-view basketball datasets demonstrate that MAEM consistently outperforms existing training-free association baselines.
• Score: 10.727074525703147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-view multi-person 3D pose estimation in team sports scenarios remains challenging due to player occlusions, appearance similarity caused by team uniforms, and the scarcity of annotated multi-view data, all of which limit the effectiveness and generalization capability of learning-based methods. In contrast, the performance of training-free approaches is inherently constrained by the accuracy of 2D keypoint detection and the robustness of cross-view association. To address these challenges, we propose Mesh-Aware Epipolar Matching (MAEM), a training-free framework for multi-view multi-person 3D pose estimation. Our method employs a monocular 3D human mesh recovery model as the frontend and introduces a two-stage epipolar matching strategy based on the recovered mesh outputs. Specifically, the proposed framework combines disjoint-set-union-based clustering with per-joint triangulation to achieve robust cross-view association and accurate 3D pose reconstruction. Experiments on two public multi-view basketball datasets demonstrate that MAEM consistently outperforms existing training-free association baselines while achieving competitive RGB-only performance in both indoor and outdoor basketball scenarios. MAEM achieves MPJPE/PA-MPJPE scores of 59.8/40.7 mm on SportCenter EPFL and 74.0/51.8 mm on Human-M3 Basketball, highlighting the effectiveness of dense mesh geometry for cross-view association without requiring target-domain training or fine-tuning.

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