This paper presents Paint3D, a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information, which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this, our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion, producing an initial coarse texture map. However, as 2D models cannot fully represent 3D shapes and disable lighting effects, the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this, we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process, Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less, significantly advancing the state-of-the-art in texturing 3D objects.

翻译：本文提出Paint3D，一种新颖的粗到细生成框架，能够根据文本或图像输入，为未纹理化的3D网格生成高分辨率、无光照且多样化的2K UV纹理贴图。其核心挑战在于生成不含嵌入式光照信息的高质量纹理，从而支持在现代图形管线中重新照明或编辑。为此，该方法首先利用预训练的深度感知2D扩散模型生成视角条件图像，并执行多视角纹理融合，得到初始粗糙纹理贴图。然而，由于2D模型无法完整表征3D形状且无法消除光照效应，粗糙纹理贴图存在区域不完整和光照伪影问题。为解决该问题，我们分别训练了专门用于形状感知精细化修复不完整区域和消除光照伪影的UV Inpainting与UVHD扩散模型。通过这种粗到细的流程，Paint3D能够生成保持语义一致性且无光照的高质量2K UV纹理，显著推动了3D物体纹理化领域的发展。