用自回归流匹配预测复杂运动轨迹，提升人机动作预判性能。
Autoregressive Flow Matching for Motion Prediction
- 基于自回归流匹配建模序列连续数据，支持长时序轨迹生成。
- 在多样化视频数据上训练，可准确预测长期运动轨迹。
- 适配人机动作预测任务，显著提升下游任务效果。
运动预测在人体动作与机器人应用中已有广泛研究，但现有模型多基于窄分布训练，泛化能力受限。尽管视频生成规模扩大带来视觉真实感提升，却难以精确建模复杂运动。受视频生成规模化启发，我们提出自回归流匹配（ARFM），一种用于连续序列数据的概率建模方法，并在多样化的视频数据集上训练，以生成长期前景点轨迹。为评估模型性能，我们构建了用于评测人机运动预测能力的基准测试。实验表明，该模型能有效预测复杂运动；且将机器人动作与人体运动预测结果以未来轨迹为条件输入，可显著提升下游任务表现。代码与模型已公开于：https://github.com/Johnathan-Xie/arfm-motion-prediction。
原文摘要 · Abstract (English)
Motion prediction has been studied in different contexts with models trained on narrow distributions and applied to downstream tasks in human motion prediction and robotics. Simultaneously, recent efforts in scaling video prediction have demonstrated impressive visual realism, yet they struggle to accurately model complex motions despite massive scale. Inspired by the scaling of video generation, we develop autoregressive flow matching (ARFM), a new method for probabilistic modeling of sequential continuous data and train it on diverse video datasets to generate future point track locations over long horizons. To evaluate our model, we develop benchmarks for evaluating the ability of motion prediction models to predict human and robot motion. Our model is able to predict complex motions, and we demonstrate that conditioning robot action prediction and human motion prediction on predicted future tracks can significantly improve downstream task performance. Code and models publicly available at: https://github.com/Johnathan-Xie/arfm-motion-prediction.
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