GUESS : GradUally Enriching SyntheSis for Text-Driven Human Motion Generation
In this article, we propose a novel cascaded diffusion-based generative framework for text-driven human motion synthesis, which exploits a strategy named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy sets up generation objectives by grouping body joints of detailed skeleton...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 12 vom: 31. Okt., Seite 7518-7530
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| 1. Verfasser: |
Gao, Xuehao
(VerfasserIn) |
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| Weitere Verfasser: |
Yang, Yang,
Xie, Zhenyu,
Du, Shaoyi,
Sun, Zhongqian,
Wu, Yang |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics
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| Schlagworte: | Journal Article |
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