发表机构
Beihang University; Durham University; Zhongguancun Laboratory（北京航空航天大学; 杜伦大学; 中关村实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对随机三维人体运动预测中忽视合理性与不确定性量化的问题，提出带数据驱动高斯混合先验的潜在流模型，在Human3.6M和AMASS数据集上实现准确性与合理性的最优性能。
AI 中文摘要
随机人体运动预测旨在预测未来的运动分布。尽管近期研究在准确性和多样性方面已取得良好性能，但它们常忽视合理性（例如产生不符合物理规律的预测）与不确定性量化，而这两者对实际应用和下游任务至关重要。为解决这些问题，我们提出一种基于潜在流的模型，配备数据驱动的高斯混合先验，该先验比传统单模态先验更能有效解耦多样的人类行为；此先验源自训练数据中的模式，无需额外标注。此外，模型的完全可逆性可通过可处理的似然计算实现自然的不确定性量化。在Human3.6M和AMASS数据集上的实验表明，我们的方法在准确性和合理性两方面均达到了最先进的性能。
英文摘要
Stochastic human motion prediction aims to forecast future motion distributions. Although recent studies have achieved strong performance in terms of accuracy and diversity, they often overlook plausibility (e.g., resulting in physically unrealistic predictions) and uncertainty quantification, both of which are essential for real-world applications and downstream tasks. To address these issues, we propose a latent flow-based model equipped with a data-driven Gaussian mixture prior that more effectively disentangles diverse human behaviors than conventional single-modal priors. This prior is derived from patterns in the training data without requiring additional annotations. Furthermore, the fully invertible nature of our model enables natural uncertainty quantification through tractable likelihood computation. Experiments on the Human3.6M and AMASS datasets demonstrate that our approach achieves state-of-the-art performance in both accuracy and plausibility.
