  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.
