FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis
<|reference_start|>FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis: 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.<|reference_end|>
@article{fan2024freemotion:,
    title={FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis},
    author={Ke Fan, Junshu Tang, Weijian Cao, Ran Yi, Moran Li, Jingyu Gong,
  Jiangning Zhang, Yabiao Wang, Chengjie Wang, Lizhuang Ma},
    journal={arXiv preprint arXiv:2405.15763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15763},
    primaryClass={cs.CV}
}