Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation

TL;DR

This paper introduces Ready-to-React, an online reaction policy for two-character interaction generation that models real-time, independent reactions, outperforming previous methods in generating extended, controllable motion sequences.

Contribution

We propose a novel online reaction policy with a diffusion head that enables real-time, independent character reactions, improving over prior joint or complete motion generation methods.

Findings

Outperforms existing baselines in boxing motion tasks

Generates extended motion sequences effectively

Allows control via sparse signals for interactive environments

Abstract

This paper addresses the task of generating two-character online interactions. Previously, two main settings existed for two-character interaction generation: (1) generating one's motions based on the counterpart's complete motion sequence, and (2) jointly generating two-character motions based on specific conditions. We argue that these settings fail to model the process of real-life two-character interactions, where humans will react to their counterparts in real time and act as independent individuals. In contrast, we propose an online reaction policy, called Ready-to-React, to generate the next character pose based on past observed motions. Each character has its own reaction policy as its "brain", enabling them to interact like real humans in a streaming manner. Our policy is implemented by incorporating a diffusion head into an auto-regressive model, which can dynamically respond to the counterpart's motions while effectively mitigating the error accumulation throughout the generation process. We conduct comprehensive experiments using the challenging boxing task. Experimental results demonstrate that our method outperforms existing baselines and can generate extended motion sequences. Additionally, we show that our approach can be controlled by sparse signals, making it well-suited for VR and other online interactive environments.