FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis

• URL: http://arxiv.org/abs/2405.15763v1
• Date: Fri, 24 May 2024 17:57:57 GMT
• Title: FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis
• Authors: Ke Fan, Junshu Tang, Weijian Cao, Ran Yi, Moran Li, Jingyu Gong, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Lizhuang Ma,
• Abstract summary: This paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion.
• Score: 65.85686550683806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-motion synthesis is a crucial task in computer vision. Existing methods are limited in their universality, as they are tailored for single-person or two-person scenarios and can not be applied to generate motions for more individuals. To achieve the number-free motion synthesis, this paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Furthermore, a generation module and an interaction module are designed for our FreeMotion framework to decouple the process of conditional motion generation and finally support the number-free motion synthesis. Besides, based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion. Extensive experiments demonstrate the superior performance of our method and our capability to infer single and multi-human motions simultaneously.

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