3D multi-person motion prediction is a challenging task that involves
modeling individual behaviors and interactions between people. Despite the
emergence of approaches for this task, comparing them is difficult due to the
lack of standardized training settings and benchmark datasets. In this paper,
we introduce the Multi-Person Interaction Motion (MI-Motion) Dataset, which
includes skeleton sequences of multiple individuals collected by motion capture
systems and refined and synthesized using a game engine. The dataset contains
167k frames of interacting people's skeleton poses and is categorized into 5
different activity scenes. To facilitate research in multi-person motion
prediction, we also provide benchmarks to evaluate the performance of
prediction methods in three settings: short-term, long-term, and
ultra-long-term prediction. Additionally, we introduce a novel baseline
approach that leverages graph and temporal convolutional networks, which has
demonstrated competitive results in multi-person motion prediction. We believe
that the proposed MI-Motion benchmark dataset and baseline will facilitate
future research in this area, ultimately leading to better understanding and
modeling of multi-person interactions.

本文介绍了一个新的数据集 MI-Motion，用来帮助研究多人运动预测，并提供基准测试来评估预测模型的性能，同时还介绍了一种新的基线方法 —— 图卷积神经网络和时间卷积神经网络。