发表机构
Tianjin University（天津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出TGRHuman方法，解耦3D人体的几何与纹理生成，采用显式多视图优化结合扩散渲染器，实现高效高质量文本引导的逼真3D人体生成，性能优于现有方法。
AI 中文摘要
逼真3D人体生成在众多图形应用中发挥着关键作用。然而，当前方法在生成高质量人体几何与纹理的同时，仍难以兼顾3D一致性与推理效率。本研究提出一种从文本生成逼真3D人体的新方法TGRHuman，通过解耦几何与纹理生成以缓解基于NeRF的方法常见问题；摒弃依赖缓慢的隐式分数蒸馏优化的方案，转而直接采用显式多视图观测生成与优化实现高效3D合成。在几何生成方面，提出高分辨率多视图法线生成模块与几何雕刻策略，既保持视图一致性又支持宽松衣物；在纹理生成方面，通过精心设计的纹理先验获取策略与扩散渲染器，从密集采样的周围视图生成空间一致的RGB观测，实现细节丰富的人体纹理合成。实验表明，该方法可高效生成高质量且一致的3D人体几何与纹理，在几何和纹理质量上均优于现有文本到3D人体方法。
英文摘要
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.
