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

Interactive Humanoid: Online Full-Body Motion Reaction Synthesis with Social Affordance Canonicalization and Forecasting