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.

本研究旨在从文本描述中生成多个人的自然和多样化的群体动作。我们利用大规模图像和视频数据集估计姿势信息，通过基于 Transformer 的扩散框架，实现了多个数据集中任意数量主题或帧的生成。实验证明，我们的方法是第一个能从多样的文本提示中生成高多样性和保真度的多主体运动序列的方法。