TeRA: Rethinking Text-driven Realistic 3D Avatar Generation

2025-09-02

AI Summary

To address the challenges of slow iterative optimization, difficulty in localized customization, and insufficient realism in text-driven 3D portrait generation, this paper proposes TeRA—a two-stage text-to-3D portrait generation framework. First, knowledge distillation compresses a large-scale human reconstruction model’s decoder into a structured latent space that encodes geometric priors. Second, a text-conditioned latent diffusion model is trained within this space, enabling end-to-end, non-iterative 3D generation. The key contribution lies in introducing a structured 3D human representation, which supports fine-grained, text-guided local editing (e.g., “wearing a red jacket” or “wearing glasses”) while avoiding the inefficiency of conventional score-distillation sampling (SDS)-based optimization. Experiments demonstrate that TeRA significantly outperforms existing methods in generation quality, photorealism, and inference speed, achieving state-of-the-art performance in both objective and subjective evaluations.

Technology Category

Computer Vision: Diffusion Models for VisionNatural Language Processing: GenerationSearch and Optimization: Local Search

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Generating realistic 3D human avatars from text descriptions
• Overcoming inefficiencies in previous SDS-based 3D generation models
• Enabling text-based partial customization of 3D human representations

Innovation

• Two-stage training strategy for 3D avatar generation
• Latent diffusion model in structured space
• Eliminates slow iterative optimization process