Multi-person motion prediction is an emerging and intricate task with broad real-world applications. Unlike single person motion prediction, it considers not just the skeleton structures or human trajectories but also the interactions between others. Previous methods use various networks to achieve impressive predictions but often overlook that the joints relations within an individual (intra-relation) and interactions among groups (inter-relation) are distinct types of representations. These methods often lack explicit representation of inter&intra-relations, and inevitably introduce undesired dependencies. To address this issue, we introduce a new collaborative framework for multi-person motion prediction that explicitly modeling these relations:a GCN-based network for intra-relations and a novel reasoning network for inter-relations.Moreover, we propose a novel plug-and-play aggregation module called the Interaction Aggregation Module (IAM), which employs an aggregate-attention mechanism to seamlessly integrate these relations. Experiments indicate that the module can also be applied to other dual-path models. Extensive experiments on the 3DPW, 3DPW-RC, CMU-Mocap, MuPoTS-3D, as well as synthesized datasets Mix1 & Mix2 (9 to 15 persons), demonstrate that our method achieves state-of-the-art performance.

翻译：多人运动预测是一项新兴且复杂的任务，具有广泛的实际应用价值。与单人运动预测不同，它不仅考虑骨架结构或人体轨迹，还需考虑个体间的交互作用。先前的方法利用各种网络实现了令人印象深刻的预测效果，但往往忽略了人体内部关节关系（内部关系）与群体间交互作用（交互关系）属于不同类型的表征。这些方法通常缺乏对交互与内部关系的显式建模，且不可避免地引入了不必要的依赖关系。为解决此问题，我们提出了一种新的多人运动预测协作框架，显式建模这两类关系：采用基于GCN的网络处理内部关系，并设计新型推理网络处理交互关系。此外，我们提出了一种新颖的即插即用聚合模块——交互聚合模块（IAM），该模块通过聚合注意力机制无缝整合这两类关系。实验表明该模块同样适用于其他双路径模型。在3DPW、3DPW-RC、CMU-Mocap、MuPoTS-3D以及合成数据集Mix1与Mix2（9至15人规模）上的大量实验证明，我们的方法实现了最先进的性能。