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.
@article{arxiv.2405.18483,
title = {Towards Open Domain Text-Driven Synthesis of Multi-Person Motions},
author = {Mengyi Shan and Lu Dong and Yutao Han and Yuan Yao and Tao Liu and Ifeoma Nwogu and Guo-Jun Qi and Mitch Hill},
journal= {arXiv preprint arXiv:2405.18483},
year = {2024}
}