面向动作驱动人体运动预测的朝向感知腿部运动学习
Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction
AI总结：
本文提出一种朝向感知的腿部运动学习方法，通过动作条件下的中间插值生成自然过渡，解决动作驱动人体运动预测中朝向变化导致的过渡不自然问题，并在多个基准上达到最优性能。
摘要：
动作驱动的人体运动预测任务旨在基于观测序列预测未来的人体运动，同时遵循给定的动作标签。该任务不仅需要对人体运动内部的随机性进行建模，还需要对多个动作标签之间的平滑且真实的过渡进行建模。然而，大多数数据集并不包含此类过渡数据，这一事实使得该任务变得复杂。现有工作通过学习平滑先验来简单地促进平滑过渡，从而解决这一问题，但这样做可能导致不自然的过渡，尤其是当历史运动与预测运动在朝向上差异显著时。在本文中，我们主张有效的人体运动过渡应包含真实的腿部运动以处理朝向变化，并将其视为一个动作条件下的中间插值（ACB）学习任务，以鼓励过渡的自然性。由于对所有可能的过渡进行建模实际上是不合理的，我们的ACB仅在少数选定的具有活跃步态动作的动作类别（如行走或跑步）上执行。具体而言，我们采用两阶段预测策略：首先使用运动扩散模型生成具有指定未来动作的目标运动，然后生成中间插值以平滑连接观测与预测，最终解决运动预测问题。我们的方法在训练过程中完全不需要标注的运动过渡数据。为了展示我们方法的鲁棒性，我们将训练好的中间插值学习模型从一个数据集泛化到两个未见的大规模运动数据集，以生成自然的过渡。在三个基准数据集上的广泛实验评估表明，我们的方法在视觉质量、预测准确性和动作忠实度方面均达到了最先进的性能。
英文摘要：
The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling not only the stochasticity within human motion but the smooth yet realistic transition between multiple action labels. However, the fact that most datasets do not contain such transition data complicates this task. Existing work tackles this issue by learning a smoothness prior to simply promote smooth transitions, yet doing so can result in unnatural transitions especially when the history and predicted motions differ significantly in orientations. In this paper, we argue that valid human motion transitions should incorporate realistic leg movements to handle orientation changes, and cast it as an action-conditioned in-betweening (ACB) learning task to encourage transition naturalness. Because modeling all possible transitions is virtually unreasonable, our ACB is only performed on very few selected action classes with active gait motions, such as Walk or Run. Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction. Our method is completely free from the labeled motion transition data during training. To show the robustness of our approach, we generalize our trained in-betweening learning model on one dataset to two unseen large-scale motion datasets to produce natural transitions. Extensive experimental evaluations on three benchmark datasets demonstrate that our method yields the state-of-the-art performance in terms of visual quality, prediction accuracy, and action faithfulness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
