流中的骨架：用于人体运动预测的图结构化流匹配

中文摘要 AI 辅助

人体运动预测需要产生多样化的未来轨迹，这些轨迹需与已观测到的运动以及身体的关节物理结构保持一致。骨骼约束限制了个体姿态，而协调运动则依赖于空间交互（连接关节之间）和时间交互（时间瞬间之间）。为应对这些约束和交互，我们引入了图结构化流匹配（GSFM），该方法通过单一条件速度场传输完整的未来骨骼轨迹。该轨迹生成一个时空骨骼图，空间和时间注意力根据骨骼关系和物理时间偏移耦合其演化过程。骨骼方向位于相对于根关节的单位球面上，切线演化在生成过程中保持输入骨骼长度不变。我们通过条件流匹配，沿着连接以最后观测姿态为中心的随机轨迹到记录的未来轨迹的测地路径，训练一个学习到的速度场。在动作捕捉表面形状档案（AMASS）数据集上的实验评估了预测精度、多样性校准和运动统计。我们通过消融研究展示了所开发架构中空间和时间消息传递的贡献。在AMASS上训练的GSFM模型在Human3.6M骨骼上无需参数更新或重新训练即可表现出竞争力，证明了其对未见骨骼结构的适用性。

英文摘要

Human motion prediction requires diverse future trajectories that remain consistent with observed motion and the articulated physical structure of the body. Skeletal constraints restrict individual poses, while coordinated motion depends on spatial interactions (between connected joints) and temporal interactions (between time instants). To facilitate human motion prediction in light of these constraints and interactions, we introduce Graph Structured Flow Matching (GSFM), which transports the complete future skeletal trajectory through a single conditional velocity field. The trajectory produces a spatiotemporal skeleton graph, and spatial and temporal attention couple its evolution according to skeletal relations and physical time offsets. Bone directions lie on unit spheres relative to a root joint, and tangent evolution preserves input bone lengths throughout generation. We train a learned velocity field through conditional flow matching along geodesic paths connecting random trajectories centered on the last-observed pose to recorded future trajectories. Experiments on the Archive of Motion capture As Surface Shapes (AMASS) dataset evaluate prediction accuracy, diversity calibration, and motion statistics. We demonstrate the contributions of spatial and temporal message passing in the developed architecture through an ablation study. GSFM models trained on AMASS also perform competitively on the Human3.6M skeleton without parameter updates or retraining, demonstrating applicability to an unseen skeletal structure.

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22 pages, 3 figures