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DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion by Lou, Yunhong; Zhu, Linchao; Wang, Yaxiong; Wang, Xiaohan; Yang, Yi is a scholarly article available to read on EtoBox.

What is DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion about?

We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions while preserving motion diversity.Despite the recent significant process in text-based human motion generation,existing methods often prioritize fitting training motions at the expense of action diversity. Consequently, striking a balance between motion quality and diversity remains an unresolved challenge. This problem is compounded by two key factors: 1) the lack of diversity in motion-caption pairs in existing benchmarks and 2) the unilateral and biased semantic understanding of the text prompt, focusing primarily on the verb component while neglecting the nuanced distinctions indicated by other words.In response to the first issue, we construct a large-scale Wild Motion-Caption dataset (WMC) to extend the restricted action boundary of existing well-annotated datasets, enabling the learning of diverse motions through a more extensive range of actions. To this end, a motion BLIP is trained upon a pretrained vision-language model, then we automatically generate diverse motion captions for the collected motion sequences. As a result, we finally build a dataset co

Author

Lou, Yunhong; Zhu, Linchao; Wang, Yaxiong; Wang, Xiaohan; Yang, Yi

Published

2023

Language

EN