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Creators/Authors contains: "Han, Yutao"

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  1. ; ; ; (, IEEE)
    Full Text Available 
  2. ; ; ; ; ; ; ; (, Springer_Science+Business_Media)
    This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While singleperson 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 multiperson 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. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Conference on Robot Learning (CoRL))