文本生成3D真人，速度快质量高，支持宽松服装
TGRHuman: Text-Guided Realistic 3D Human Generation via Diffusion Renderer
- 分离几何与纹理生成，用多视角显式优化提升效率
- 生成高分辨率法线图并保留视角一致性，支持松散衣物
- 结合扩散渲染与纹理先验，实现细节丰富的真实纹理
真实感3D人类生成在图形应用中至关重要。然而，现有方法在保持3D一致性与推理效率的同时，仍难以生成高质量的几何与纹理。本文提出TGRHuman，一种从文本生成逼真3D人类的新方法。该方法解耦几何与纹理生成，缓解基于NeRF方法的常见问题。不依赖慢速的隐式得分蒸馏优化，而是直接采用显式的多视角观测生成与优化，实现高效3D合成。几何生成方面，提出高分辨率多视角法线生成模块，并结合几何雕刻策略，保持视角一致性且支持宽松服装。纹理生成方面，通过密集采样周围视角，利用精心设计的纹理先验获取策略与扩散渲染器，生成空间一致的RGB观测，实现细节丰富的纹理合成。实验表明，该方法能高效生成高质量且一致的3D人体几何与纹理，在几何与纹理质量上优于现有文本到3D人类生成方法。
原文摘要 · Abstract (English)
Realistic 3D human generation plays a crucial role in many graphics applications. However, current methods still struggle to generate high-quality human geometry and texture while maintaining 3D consistency and inference efficiency. In this work, we address these limitations by introducing TGRHuman, a novel approach for generating realistic 3D humans from text. Our method decouples geometry and texture generation to alleviate the issues commonly encountered in NeRF-based methods. Instead of relying on slow, implicit score-distillation-based optimization, we directly use explicit multi-view observation generation and optimization for efficient 3D synthesis. For geometry generation, we propose a high-resolution generative module for multi-view normals together with a geometry-carving strategy that preserves view consistency and supports loose clothing. For texture generation, we produce spatially consistent RGB observations from densely sampled surrounding views using a carefully designed texture-prior acquisition strategy and a diffusion renderer, enabling detailed human texture synthesis. Experiments show that our method can generate high-quality and consistent 3D human geometry and texture efficiently. TGRHuman outperforms existing text-to-3D human methods in both geometry and texture quality.
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