MVPaint：用于绘制任何3D物体的同步多视角扩散

MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D

November 4, 2024

作者: Wei Cheng, Juncheng Mu, Xianfang Zeng, Xin Chen, Anqi Pang, Chi Zhang, Zhibin Wang, Bin Fu, Gang Yu, Ziwei Liu, Liang Pan

cs.AI

摘要

纹理处理是3D资产制作工作流程中至关重要的一步，它提升了3D资产的视觉吸引力和多样性。尽管最近在文本到纹理（T2T）生成方面取得了进展，但现有方法通常产生次优结果，主要是由于局部不连续性、多视角之间的不一致性以及对UV展开结果的严重依赖。为了解决这些挑战，我们提出了一种名为MVPaint的新型生成-细化3D纹理框架，它可以生成高分辨率、无缝纹理，同时强调多视角一致性。MVPaint主要包括三个关键模块。1）同步多视角生成（SMG）。给定一个3D网格模型，MVPaint首先通过采用SMG模型同时生成多视角图像，导致粗糙的纹理结果以及未涂色部分由于缺失观察而产生。2）空间感知3D修补（S3I）。为了确保完整的3D纹理，我们引入了S3I方法，专门设计用于有效地纹理先前未观察到的区域。3）UV细化（UVR）。此外，MVPaint采用UVR模块来提高UV空间中的纹理质量，首先执行UV空间超分辨率，然后通过空间感知缝合平滑算法来修正由UV展开引起的空间纹理不连续性。此外，我们建立了两个T2T评估基准：Objaverse T2T基准和GSO T2T基准，分别基于Objaverse数据集和整个GSO数据集中选定的高质量3D网格。广泛的实验结果表明，MVPaint超越了现有的最先进方法。值得注意的是，MVPaint能够生成高保真度纹理，减少了Janus问题，并显著增强了跨视角一致性。

English

Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and their heavy dependence on UV unwrapping outcomes. To tackle these challenges, we propose a novel generation-refinement 3D texturing framework called MVPaint, which can generate high-resolution, seamless textures while emphasizing multi-view consistency. MVPaint mainly consists of three key modules. 1) Synchronized Multi-view Generation (SMG). Given a 3D mesh model, MVPaint first simultaneously generates multi-view images by employing an SMG model, which leads to coarse texturing results with unpainted parts due to missing observations. 2) Spatial-aware 3D Inpainting (S3I). To ensure complete 3D texturing, we introduce the S3I method, specifically designed to effectively texture previously unobserved areas. 3) UV Refinement (UVR). Furthermore, MVPaint employs a UVR module to improve the texture quality in the UV space, which first performs a UV-space Super-Resolution, followed by a Spatial-aware Seam-Smoothing algorithm for revising spatial texturing discontinuities caused by UV unwrapping. Moreover, we establish two T2T evaluation benchmarks: the Objaverse T2T benchmark and the GSO T2T benchmark, based on selected high-quality 3D meshes from the Objaverse dataset and the entire GSO dataset, respectively. Extensive experimental results demonstrate that MVPaint surpasses existing state-of-the-art methods. Notably, MVPaint could generate high-fidelity textures with minimal Janus issues and highly enhanced cross-view consistency.