TeRA: Rethinking Text-guided Realistic 3D Avatar Generation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915476900151296 |
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| author | Wang, Yanwen Zhuang, Yiyu Zhang, Jiawei Wang, Li Zeng, Yifei Cao, Xun Zuo, Xinxin Zhu, Hao |
| author_facet | Wang, Yanwen Zhuang, Yiyu Zhang, Jiawei Wang, Li Zeng, Yifei Cao, Xun Zuo, Xinxin Zhu, Hao |
| contents | In this paper, we rethink text-to-avatar generative models by proposing TeRA, a more efficient and effective framework than the previous SDS-based models and general large 3D generative models. Our approach employs a two-stage training strategy for learning a native 3D avatar generative model. Initially, we distill a decoder to derive a structured latent space from a large human reconstruction model. Subsequently, a text-controlled latent diffusion model is trained to generate photorealistic 3D human avatars within this latent space. TeRA enhances the model performance by eliminating slow iterative optimization and enables text-based partial customization through a structured 3D human representation. Experiments have proven our approach's superiority over previous text-to-avatar generative models in subjective and objective evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02466 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TeRA: Rethinking Text-guided Realistic 3D Avatar Generation Wang, Yanwen Zhuang, Yiyu Zhang, Jiawei Wang, Li Zeng, Yifei Cao, Xun Zuo, Xinxin Zhu, Hao Computer Vision and Pattern Recognition In this paper, we rethink text-to-avatar generative models by proposing TeRA, a more efficient and effective framework than the previous SDS-based models and general large 3D generative models. Our approach employs a two-stage training strategy for learning a native 3D avatar generative model. Initially, we distill a decoder to derive a structured latent space from a large human reconstruction model. Subsequently, a text-controlled latent diffusion model is trained to generate photorealistic 3D human avatars within this latent space. TeRA enhances the model performance by eliminating slow iterative optimization and enables text-based partial customization through a structured 3D human representation. Experiments have proven our approach's superiority over previous text-to-avatar generative models in subjective and objective evaluation. |
| title | TeRA: Rethinking Text-guided Realistic 3D Avatar Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02466 |