We address the challenging task of human reaction generation, which aims to
generate a corresponding reaction based on an input action. Most of the
existing works do not focus on generating and predicting the reaction and
cannot generate the motion when only the action is given as input. To address
this limitation, we propose a novel interaction Transformer (InterFormer)
consisting of a Transformer network with both temporal and spatial attention.
Specifically, temporal attention captures the temporal dependencies of the
motion of both characters and of their interaction, while spatial attention
learns the dependencies between the different body parts of each character and
those which are part of the interaction. Moreover, we propose using graphs to
increase the performance of spatial attention via an interaction distance
module that helps focus on nearby joints from both characters. Extensive
experiments on the SBU interaction, K3HI, and DuetDance datasets demonstrate
the effectiveness of InterFormer. Our method is general and can be used to
generate more complex and long-term interactions. We also provide videos of
generated reactions and the code with pre-trained models at
this https URL

本研究提出了一种基于交互 Transformer 的新模型，该模型结合了时间和空间的注意力机制，通过使用图形和交互距离模块进一步提高了空间注意力的性能，可用于生成更复杂和长期的交互动作。