SMooDi: Stylized Motion Diffusion Model
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866907982728527872 |
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| author | Zhong, Lei Xie, Yiming Jampani, Varun Sun, Deqing Jiang, Huaizu |
| author_facet | Zhong, Lei Xie, Yiming Jampani, Varun Sun, Deqing Jiang, Huaizu |
| contents | We introduce a novel Stylized Motion Diffusion model, dubbed SMooDi, to generate stylized motion driven by content texts and style motion sequences. Unlike existing methods that either generate motion of various content or transfer style from one sequence to another, SMooDi can rapidly generate motion across a broad range of content and diverse styles. To this end, we tailor a pre-trained text-to-motion model for stylization. Specifically, we propose style guidance to ensure that the generated motion closely matches the reference style, alongside a lightweight style adaptor that directs the motion towards the desired style while ensuring realism. Experiments across various applications demonstrate that our proposed framework outperforms existing methods in stylized motion generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12783 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SMooDi: Stylized Motion Diffusion Model Zhong, Lei Xie, Yiming Jampani, Varun Sun, Deqing Jiang, Huaizu Computer Vision and Pattern Recognition Graphics We introduce a novel Stylized Motion Diffusion model, dubbed SMooDi, to generate stylized motion driven by content texts and style motion sequences. Unlike existing methods that either generate motion of various content or transfer style from one sequence to another, SMooDi can rapidly generate motion across a broad range of content and diverse styles. To this end, we tailor a pre-trained text-to-motion model for stylization. Specifically, we propose style guidance to ensure that the generated motion closely matches the reference style, alongside a lightweight style adaptor that directs the motion towards the desired style while ensuring realism. Experiments across various applications demonstrate that our proposed framework outperforms existing methods in stylized motion generation. |
| title | SMooDi: Stylized Motion Diffusion Model |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2407.12783 |