CTGAN: Semantic-guided Conditional Texture Generator for 3D Shapes
Yi-Ting Pan Chai-Rong Lee Shu-Ho Fan Jheng-Wei Su Jia-Bin Huang Yung-Yu Chuang Hung-Kuo Chu
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.