Human motion prediction aims to forecast future human poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackled for single humans in isolation. In this paper we explore this problem from a novel perspective, involving humans performing collaborative tasks. We assume that the input of our system are two sequences of past skeletons for two interacting persons, and we aim to predict the future motion for each of them. For this purpose, we devise a novel cross interaction attention mechanism that exploits historical information of both persons and learns to predict cross dependencies between self poses and the poses of the other person in spite of their spatial or temporal distance. Since no dataset to train such interactive situations is available, we have captured ExPI (Extreme Pose Interaction), a new lab-based person interaction dataset of professional dancers performing acrobatics. ExPI contains 115 sequences with 30k frames and 60k instances with annotated 3D body poses and shapes. We thoroughly evaluate our cross-interaction network on this dataset and show that both in short-term and long-term predictions, it consistently outperforms baselines that independently reason for each person. We plan to release our code jointly with the dataset and the train/test splits to spur future research on the topic.

本文旨在预测两个互动人物的运动，通过收集一组专业舞蹈者的数据，提出了一种新的跨交互关注机制，在短期和长期预测中都表现出优于单人运动预测的现有方法。

多人极端动作预测