Abstract
Generating high-fidelity, seamless textures directly on 3D surfaces, what we term 3D-native texturing, remains a fundamental open challenge, with the potential to overcome long-standing limitations of UV-based and multi-view projection methods. However, existing native approaches are constrained by the absence of a powerful and versatile latent representation, which severely limits the fidelity and generality of their generated textures. We identify this representation gap as the principal barrier to further progress. We introduce LaFiTe, a framework that addresses this challenge by learning to generate textures as a 3D generative sparse latent color field. At its core, LaFiTe employs a variational autoencoder (VAE) to encode complex surface appearance into a sparse, structured latent space, which is subsequently decoded into a continuous color field. This representation achieves unprecedented fidelity, exceeding state-of-the-art methods by >10 dB PSNR in reconstruction, by effectively disentangling texture appearance from mesh topology and UV parameterization. Building upon this strong representation, a conditional rectified-flow model synthesizes high-quality, coherent textures across diverse styles and geometries. Extensive experiments demonstrate that LaFiTe not only sets a new benchmark for 3D-native texturing but also enables flexible downstream applications such as material synthesis and texture super-resolution, paving the way for the next generation of 3D content creation workflows.
Abstract (translated)
生成高质量、无缝贴图直接在三维表面上进行，我们称之为原生三维纹理化，仍然是一个基本的开放挑战。这种方法有可能克服基于UV映射和多视角投影方法长期以来存在的局限性。然而，现有的原生方法由于缺乏强大且通用的潜在表示而受到限制，这严重制约了它们生成的贴图质量和适用性。我们认为这一表示差距是进一步发展的主要障碍。 为此，我们引入了一种名为LaFiTe（Latent Fidelity Texturing）的新框架，它通过学习将纹理作为三维生成稀疏潜在颜色场来解决这一挑战。LaFiTe的核心采用变分自编码器(VAE)技术，将复杂的表面外观编码进一个稀疏且结构化的潜在空间中，并进一步解码为连续的颜色字段。这种表示方法实现了前所未有的保真度，在重建时超过了当前最佳方法10dB以上的PSNR（峰值信噪比），通过有效地分离纹理外观与网格拓扑和UV参数化，从而提高质量。 在此强大的表示基础上，条件正流模型能够合成出高质量、连贯的贴图，涵盖各种风格和几何形态。一系列广泛的实验表明，LaFiTe不仅为原生三维纹理化设立了新的基准，而且还支持了诸如材质生成和纹理超分辨率等灵活的下游应用的发展，从而开启了下一代三维内容创建工作流程的大门。
URL
https://arxiv.org/abs/2512.04786
