Towards Open Domain Text-Driven Synthesis of Multi-Person Motions
<|reference_start|>Towards Open Domain Text-Driven Synthesis of Multi-Person Motions: This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While single-person text-to-motion generation is extensively studied, it remains challenging to synthesize motions for more than one or two subjects from in-the-wild prompts, mainly due to the lack of available datasets. In this work, we curate human pose and motion datasets by estimating pose information from large-scale image and video datasets. Our models use a transformer-based diffusion framework that accommodates multiple datasets with any number of subjects or frames. Experiments explore both generation of multi-person static poses and generation of multi-person motion sequences. To our knowledge, our method is the first to generate multi-subject motion sequences with high diversity and fidelity from a large variety of textual prompts.<|reference_end|>
@article{shan2024towards,
    title={Towards Open Domain Text-Driven Synthesis of Multi-Person Motions},
    author={Mengyi Shan, Lu Dong, Yutao Han, Yuan Yao, Tao Liu, Ifeoma Nwogu,
  Guo-Jun Qi, and Mitch Hill},
    journal={arXiv preprint arXiv:2405.18483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18483},
    primaryClass={cs.CV}
}