新方法通过联合建模轨迹和3D姿态来预测人类运动

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种新的方法，通过联合建模智能体的全局轨迹和3D身体姿态来预测体育运动中的人类运动。该方法利用了一个社会-时间编码器，该编码器在输入层面融合了轨迹和姿态信息，并通过共享的因子化注意力进行处理。该模型包含了一个每关节损失重加权方案，以强调动态关节，以及一个频域轨迹平滑项，以提高预测精度。研究人员还通过采用非重叠协议和多未来预测的确定性策略来解决评估偏差，并在WorldPose和3DPW数据集上取得了改进的结果，尤其是在姿态准确性方面。 AI

影响这项研究可能导致对体育运动中球员运动进行更准确的分析和预测，从而可能影响教练、表现分析甚至体育广播。

排序理由该集群包含一篇详细介绍运动预测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Marc Peral, Guillem Capellera, Luis Ferraz, Antonio Rubio, Stella Gra{\ss}hof, Dan Witzner Hansen, Antonio Agudo · 2026-10-23 04:00

Unified Trajectory and 3D Pose Forecasting for Multi-Agent Sports Analysis

arXiv:2610.30519v1 Announce Type: new Abstract: Forecasting future human motion requires jointly reasoning about an agent's global trajectory and 3D body pose, which are tightly coupled, particularly in team sports where multiple interacting players introduce complex social dynam…

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Unified Trajectory and 3D Pose Forecasting for Multi-Agent Sports Analysis

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