  We focus on the human-humanoid interaction task optionally with an object. We
propose a new task named online full-body motion reaction synthesis, which
generates humanoid reactions based on the human actor's motions. The previous
work only focuses on human interaction without objects and generates body
reactions without hand. Besides, they also do not consider the task as an
online setting, which means the inability to observe information beyond the
current moment in practical situations. To support this task, we construct two
datasets named HHI and CoChair and propose a unified method. Specifically, we
propose to construct a social affordance representation. We first select a
social affordance carrier and use SE(3)-Equivariant Neural Networks to learn
the local frame for the carrier, then we canonicalize the social affordance.
Besides, we propose a social affordance forecasting scheme to enable the
reactor to predict based on the imagined future. Experiments demonstrate that
our approach can effectively generate high-quality reactions on HHI and
CoChair. Furthermore, we also validate our method on existing human interaction
datasets Interhuman and Chi3D.
