TeRA: Rethinking Text-driven Realistic 3D Avatar Generation

• URL: http://arxiv.org/abs/2509.02466v1
• Date: Tue, 02 Sep 2025 16:20:20 GMT
• Title: TeRA: Rethinking Text-driven Realistic 3D Avatar Generation
• Authors: Yanwen Wang, Yiyu Zhuang, Jiawei Zhang, Li Wang, Yifei Zeng, Xun Cao, Xinxin Zuo, Hao Zhu,
• Abstract summary: TeRA is a more efficient and effective framework than the previous SDS-based models and general large 3D generative models.<n>Our approach employs a two-stage training strategy for learning a native 3D avatar generative model.<n> Experiments have proven our approach's superiority over previous text-to-avatar generative models in subjective and objective evaluation.
• Score: 33.93081373817039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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