Texturing 3D meshes plays a vital role in determining the visual realism of digital objects and scenes. Although recent generative 3D approaches based on Neural Radiance Fields and Gaussian Splatting can produce textured assets directly, polygonal meshes remain the core representation across modeling, animation, visual effects, and gaming pipelines. Neural 3D mesh texturing therefore continues to be an essential and active area of research. In this survey, we present a comprehensive review of recent advances in neural 3D mesh texturing, covering methods for texture synthesis, transfer, and completion. We first summarize key foundations in mesh geometry, texture mapping, differentiable rendering, and neural generative models, and then organize the literature into a unified taxonomy spanning early GAN-based methods to modern diffusion-based pipelines. We further analyze common architectures and supervision strategies, review datasets and evaluation protocols, and discuss emerging applications, practical/commercial systems, and open challenges. Together, these insights provide a structured perspective on the current landscape and help guide future developments in learning-based 3D mesh texturing.

翻译：三维网格纹理化在决定数字对象与场景的视觉真实感中起着关键作用。尽管近年来基于神经辐射场和高斯泼溅技术的生成式三维方法可以直接生成带纹理的资产，但多边形网格仍然是建模、动画、视觉特效和游戏管线中的核心表示方式。因此，神经三维网格纹理化依然是一个重要且活跃的研究领域。本综述对神经三维网格纹理化的最新进展进行了全面回顾，涵盖了纹理合成、迁移和补全等方法。我们首先总结了网格几何、纹理映射、可微渲染以及神经生成模型等基础理论，随后将相关文献组织成一个统一的分类体系，涵盖从早期基于生成对抗网络的方法到现代基于扩散模型的管线。我们还分析了常见的架构和监督策略，回顾了数据集和评估协议，并讨论了新兴应用、实用/商业系统以及开放挑战。通过这些见解，本文为当前研究格局提供了结构化的视角，有助于引导基于学习的三维网格纹理化的未来发展。