用级联扩散模型直接生成真实感材质图，无需隐式参数化。
CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shading
- 采用级联扩散模型与显式纹理参数化，提升生成质量。
- 在公开基准上优于现有优化方法，生成纹理在不同光照下更真实。
- 避免复杂正则化，直接输出高质量材质图，适合图形渲染应用。
本文研究基于扩散模型的文本到材质图生成，旨在实现不同光照条件下逼真的模型外观。当前主流方法为评分蒸馏采样（score distillation sampling），结合可微分光栅化与着色管线，通过梯度引导恢复复杂纹理。然而，该方法与广泛使用的潜在扩散模型结合时会产生严重视觉伪影，需额外正则化如隐式纹理参数化。为此，我们提出级联扩散纹理生成方法（CasTex），在设定中直接使用评分蒸馏采样即可获得高质量纹理。特别地，我们采用显式纹理参数化替代隐式方式，简化流程。实验表明，本方法在公开纹理生成基准上显著优于现有基于优化的先进方法。
原文摘要 · Abstract (English)
This work investigates text-to-texture synthesis using diffusion models to generate physically-based texture maps. We aim to achieve realistic model appearances under varying lighting conditions. A prominent solution for the task is score distillation sampling. It allows recovering a complex texture using gradient guidance given a differentiable rasterization and shading pipeline. However, in practice, the aforementioned solution in conjunction with the widespread latent diffusion models produces severe visual artifacts and requires additional regularization such as implicit texture parameterization. As a more direct alternative, we propose an approach using cascaded diffusion models for texture synthesis (CasTex). In our setup, score distillation sampling yields high-quality textures out-of-the box. In particular, we were able to omit implicit texture parameterization in favor of an explicit parameterization to improve the procedure. In the experiments, we show that our approach significantly outperforms state-of-the-art optimization-based solutions on public texture synthesis benchmarks.
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