Abstract
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: this https URL.
Abstract (translated)
在不同背景下，人们已经对运动预测进行了研究，并且通过使用窄分布训练的模型应用于下游的人体动作预测和机器人任务。同时，最近在扩展视频预测方面的努力展示了令人印象深刻的视觉逼真度，但它们难以准确地建模复杂的运动，尽管规模庞大。受到视频生成规模化工作的启发，我们开发了一种新的序列连续数据概率建模方法——自回归流匹配（ARFM），并利用多样化的视频数据集对其进行训练，以在长时间范围内生成未来的点轨迹位置。 为了评估我们的模型，我们为评估运动预测模型的性能制定了基准测试标准，用于衡量它们在预测人和机器人动作方面的能力。我们的模型能够预测复杂的运动，并且通过将机器人行为预测和人体动作预测条件化于预测出的未来轨迹上，可以显著提高下游任务的表现。 代码和模型可在以下网址公开获取：[提供的是一个占位符URL "this https URL"，请替换为实际链接]。 这段文字介绍了自回归流匹配（ARFM）方法及其在运动预测中的应用，并且说明了通过使用该技术能够提升机器人与人类动作预测的准确性。
URL
https://arxiv.org/abs/2512.22688
