Mesh texture synthesis is a key component in the automatic generation of 3D content. Existing learning-based methods have drawbacks -- either by disregarding the shape manifold during texture generation or by requiring a large number of different views to mitigate occlusion-related inconsistencies. In this paper, we present a novel surface-aware approach for mesh texture synthesis that overcomes these drawbacks by leveraging the pre-trained weights of 2D Convolutional Neural Networks (CNNs) with the same architecture, but with convolutions designed for 3D meshes. Our proposed network keeps track of the oriented patches surrounding each texel, enabling seamless texture synthesis and retaining local similarity to classical 2D convolutions with square kernels. Our approach allows us to synthesize textures that account for the geometric content of mesh surfaces, eliminating discontinuities and achieving comparable quality to 2D image synthesis algorithms. We compare our approach with state-of-the-art methods where, through qualitative and quantitative evaluations, we demonstrate that our approach is more effective for a variety of meshes and styles, while also producing visually appealing and consistent textures on meshes.

翻译：网格纹理合成是三维内容自动生成中的关键组成部分。现有基于学习的方法存在缺陷——要么在纹理生成过程中忽略形状流形，要么需要大量不同视角以缓解遮挡相关的不一致性。本文提出一种新颖的曲面感知网格纹理合成方法，通过利用具有相同架构但针对三维网格设计卷积层的二维卷积神经网络(CNN)预训练权重，克服了上述缺陷。我们提出的网络追踪每个纹理像素周围的定向面片，实现了无缝纹理合成，并保留了与传统方形核二维卷积相似的局部特性。该方法能够合成考虑网格曲面几何内容的纹理，消除不连续性并达到与二维图像合成算法相当的质量。通过与最新方法的定性和定量评估比较，我们证明该方法对多种网格和风格更为有效，同时能在网格上生成视觉美观且一致的纹理。