用多相机+物理约束，精准追踪格斗赛中多人动作。
Multi-person Physics-based Pose Estimation for Combat Sports
- 用Transformer和几何约束实现跨视角人体追踪。
- 在拳击数据集上达到当前最佳精度，显著提升遮挡下表现。
- 适合体育分析、动作捕捉与运动科学领域研究者。
我们提出一种基于稀疏多相机系统的新型框架，用于格斗运动中高精度3D人体姿态估计。方法采用基于Transformer的自顶向下多视角2D姿态跟踪，结合对极几何约束与长期视频目标分割，实现跨视角身份一致性追踪。初始3D姿态通过加权三角测量与样条平滑获得，再经运动学优化提升精度。进一步引入多人物理驱动轨迹优化，有效应对快速运动、遮挡与近距离互动等挑战。在多个数据集（包括新发布的顶级拳击视频基准）上的实验结果表明，该方法达到当前最优性能。此外，我们公开了全面标注的视频数据集，以推动格斗运动多人体姿态估计的后续研究。
原文摘要 · Abstract (English)
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.
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