FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis
Ke Fan, Junshu Tang, Weijian Cao, Ran Yi*, Moran Li, Jingyu Gong, Jiangning Zhang, Yabiao Wang
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."
BibTeX
@inproceedings{eccv2024_freemotionaunifi,
title = {FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis},
author = {Ke Fan and Junshu Tang and Weijian Cao and Ran Yi* and Moran Li and Jingyu Gong and Jiangning Zhang and Yabiao Wang and Chengjie Wang and Lizhuang Ma*},
booktitle = {ECCV 2024},
year = {2024}
}