Predicting diverse human motions given a sequence of historical poses has received increasing attention. Despite rapid progress, existing work captures the multi-modal nature of human motions primarily through likelihood-based sampling, where the mode collapse has been widely observed. In this paper, we propose a simple yet effective approach that disentangles randomly sampled codes with a deterministic learnable component named anchors to promote sample precision and diversity. Anchors are further factorized into spatial anchors and temporal anchors, which provide attractively interpretable control over spatial-temporal disparity. In principle, our spatial-temporal anchor-based sampling (STARS) can be applied to different motion predictors. Here we propose an interaction-enhanced spatial-temporal graph convolutional network (IE-STGCN) that encodes prior knowledge of human motions (e.g., spatial locality), and incorporate the anchors into it. Extensive experiments demonstrate that our approach outperforms state of the art in both stochastic and deterministic prediction, suggesting it as a unified framework for modeling human motions. Our code and pretrained models are available at https://github.com/Sirui-Xu/STARS.

本文提出了一种称为STARS的新颖方法，利用确定性可学习的组件（锚点）来提高样本精度和多样性，在不同运动预测器中应用。同时，还提出了一种新的作用增强的空间-时间图卷积网络（IE-STGCN），并将锚点集成到其中，以提高对人类运动建模的效果。实验结果表明，该方法在随机和确定性预测方面均优于现有技术水平，是建模人体运动的统一框架。

多层空间时域锚点引导的多样化人体动作预测