Leader and Follower: Interactive Motion Generation under Trajectory Constraints

TL;DR

This paper introduces a novel leader-follower framework for interactive motion generation that effectively adheres to specified trajectories, improving realism and accuracy without retraining models.

Contribution

It proposes a training-free, role-based approach using a Pace Controller and Kinematic Synchronization Adapter to enhance trajectory adherence in interactive motion generation.

Findings

Outperforms existing methods in realism and accuracy

Effectively controls motion trajectory without retraining

Enhances flexibility and interactivity in two-actor scenarios

Abstract

With the rapid advancement of game and film production, generating interactive motion from texts has garnered significant attention due to its potential to revolutionize content creation processes. In many practical applications, there is a need to impose strict constraints on the motion range or trajectory of virtual characters. However, existing methods that rely solely on textual input face substantial challenges in accurately capturing the user's intent, particularly in specifying the desired trajectory. As a result, the generated motions often lack plausibility and accuracy. Moreover, existing trajectory - based methods for customized motion generation rely on retraining for single - actor scenarios, which limits flexibility and adaptability to different datasets, as well as interactivity in two-actor motions. To generate interactive motion following specified trajectories, this paper decouples complex motion into a Leader - Follower dynamic, inspired by role allocation in partner dancing. Based on this framework, this paper explores the motion range refinement process in interactive motion generation and proposes a training-free approach, integrating a Pace Controller and a Kinematic Synchronization Adapter. The framework enhances the ability of existing models to generate motion that adheres to trajectory by controlling the leader's movement and correcting the follower's motion to align with the leader. Experimental results show that the proposed approach, by better leveraging trajectory information, outperforms existing methods in both realism and accuracy.