We propose a novel framework for multi-person 3D motion trajectory
prediction. Our key observation is that a human's action and behaviors may
highly depend on the other persons around. Thus, instead of predicting each
human pose trajectory in isolation, we introduce a Multi-Range Transformers
model which contains of a local-range encoder for individual motion and a
global-range encoder for social interactions. The Transformer decoder then
performs prediction for each person by taking a corresponding pose as a query
which attends to both local and global-range encoder features. Our model not
only outperforms state-of-the-art methods on long-term 3D motion prediction,
but also generates diverse social interactions. More interestingly, our model
can even predict 15-person motion simultaneously by automatically dividing the
persons into different interaction groups. Project page with code is available
at this https URL

我们提出了一种适用于多人 3D 运动轨迹预测的新型框架，使用包含局部范围编码器和全局范围编码器的 Multi-Range Transformers 模型，可以实现不同人之间的社交互动，且在长期运动预测方面优于现有方法，甚至可以自动将人分成不同的互动组以预测 15 人的运动轨迹。