We present Paint-it, a text-driven high-fidelity texture map synthesis method for 3D meshes via neural re-parameterized texture optimization. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization, exploiting the Score-Distillation Sampling (SDS). We observe that directly applying SDS yields undesirable texture quality due to its noisy gradients. We reveal the importance of texture parameterization when using SDS. Specifically, we propose Deep Convolutional Physically-Based Rendering (DC-PBR) parameterization, which re-parameterizes the physically-based rendering (PBR) texture maps with randomly initialized convolution-based neural kernels, instead of a standard pixel-based parameterization. We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS. In experiments, Paint-it obtains remarkable quality PBR texture maps within 15 min., given only a text description. We demonstrate the generalizability and practicality of Paint-it by synthesizing high-quality texture maps for large-scale mesh datasets and showing test-time applications such as relighting and material control using a popular graphics engine. Project page: https://kim-youwang.github.io/paint-it

通过神经重参数纹理优化，我们提出了一种基于文本的高保真度纹理映射合成方法Paint-it。通过优化合成文本描述中的纹理映射，利用分数蒸馏采样(SDS)，并使用深度卷积物理渲染(DC-PBR)重新参数化基于物理的渲染纹理映射，以获得杂质滤波和优化课程调度，并能够在短时间内生成出色质量的PBR纹理映射。

Paint-it: 基于深度卷积纹理地图优化和物理渲染的文本生成纹理合成