T3M: Text Guided 3D Human Motion Synthesis from Speech
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910620000976896 |
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| author | Peng, Wenshuo Zhang, Kaipeng Zhang, Sai Qian |
| author_facet | Peng, Wenshuo Zhang, Kaipeng Zhang, Sai Qian |
| contents | Speech-driven 3D motion synthesis seeks to create lifelike animations based on human speech, with potential uses in virtual reality, gaming, and the film production. Existing approaches reply solely on speech audio for motion generation, leading to inaccurate and inflexible synthesis results. To mitigate this problem, we introduce a novel text-guided 3D human motion synthesis method, termed \textit{T3M}. Unlike traditional approaches, T3M allows precise control over motion synthesis via textual input, enhancing the degree of diversity and user customization. The experiment results demonstrate that T3M can greatly outperform the state-of-the-art methods in both quantitative metrics and qualitative evaluations. We have publicly released our code at \href{https://github.com/Gloria2tt/T3M.git}{https://github.com/Gloria2tt/T3M.git} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12885 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | T3M: Text Guided 3D Human Motion Synthesis from Speech Peng, Wenshuo Zhang, Kaipeng Zhang, Sai Qian Computer Vision and Pattern Recognition Speech-driven 3D motion synthesis seeks to create lifelike animations based on human speech, with potential uses in virtual reality, gaming, and the film production. Existing approaches reply solely on speech audio for motion generation, leading to inaccurate and inflexible synthesis results. To mitigate this problem, we introduce a novel text-guided 3D human motion synthesis method, termed \textit{T3M}. Unlike traditional approaches, T3M allows precise control over motion synthesis via textual input, enhancing the degree of diversity and user customization. The experiment results demonstrate that T3M can greatly outperform the state-of-the-art methods in both quantitative metrics and qualitative evaluations. We have publicly released our code at \href{https://github.com/Gloria2tt/T3M.git}{https://github.com/Gloria2tt/T3M.git} |
| title | T3M: Text Guided 3D Human Motion Synthesis from Speech |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.12885 |