The entertainment industry relies on 3D visual content to create immersive
experiences, but traditional methods for creating textured 3D models can be
time-consuming and subjective. Generative networks such as StyleGAN have
advanced image synthesis, but generating 3D objects with high-fidelity textures
is still not well explored, and existing methods have limitations. We propose
the Semantic-guided Conditional Texture Generator (CTGAN), producing
high-quality textures for 3D shapes that are consistent with the viewing angle
while respecting shape semantics. CTGAN utilizes the disentangled nature of
StyleGAN to finely manipulate the input latent codes, enabling explicit control
over both the style and structure of the generated textures. A coarse-to-fine
encoder architecture is introduced to enhance control over the structure of the
resulting textures via input segmentation. Experimental results show that CTGAN
outperforms existing methods on multiple quality metrics and achieves
state-of-the-art performance on texture generation in both conditional and
unconditional settings.

本研究提出了基于语义指导的条件纹理生成器（CTGAN），用于生成具有与视角一致且尊重形状语义的高质量 3D 纹理，通过精细操作潜在编码实现对生成纹理的风格和结构的显式控制，并通过输入分割进一步增强对纹理结构的控制，实验证明 CTGAN 在多个质量评估指标上优于现有的方法，并在有条件和无条件场景下实现了最先进的纹理生成性能。