SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps.

Area of Science:

• Computer Graphics
• Artificial Intelligence
• Machine Learning

Background:

• Real-time graphics demand high-quality, memory-efficient textures for virtual environment realism.
• Manual texture creation by artists is time-consuming and limits scalability.
• Existing methods often focus on synthesis or tileability, not both.

Purpose of the Study:

• To present SeamlessGAN, an automated method for generating tileable texture maps.
• To address both texture synthesis and tileability simultaneously.
• To enable efficient creation of realistic textures for real-time applications.

Main Methods:

• SeamlessGAN utilizes a generative network with a tiled latent space and adversarial expansion.
• A discriminator acts as a perceptual error metric to identify artifact-free textures.
• The model supports multi-layered texture representations (e.g., albedo, normals).

Main Results:

• SeamlessGAN successfully generates visually realistic and seamlessly tileable textures from single exemplars.
• The method outperforms previous texture synthesis techniques quantitatively and qualitatively.
• The approach is effective across various texture types and supports multi-layered maps.

Conclusions:

• SeamlessGAN offers an effective, automated solution for generating high-quality, tileable textures.
• The method enhances realism and reduces manual effort in virtual environment development.
• This work advances deep texture synthesis for real-time graphics applications.