Deep Tiling: Texture Tile Synthesis Using a Deep Learning Approach
This addresses a memory limitation in computer graphics for texture synthesis, offering an incremental improvement over existing deep learning methods.
The paper tackles the problem of generating high-resolution textures for 3D models by proposing a deep learning method that synthesizes small tiles to expand textures, reducing GPU memory usage and enabling the creation of missing parts in large textures.
Texturing is a fundamental process in computer graphics. Texture is leveraged to enhance the visualization outcome for a 3D scene. In many cases a texture image cannot cover a large 3D model surface because of its small resolution. Conventional techniques like repeating, mirror repeating or clamp to edge do not yield visually acceptable results. Deep learning based texture synthesis has proven to be very effective in such cases. All deep texture synthesis methods trying to create larger resolution textures are limited in terms of GPU memory resources. In this paper, we propose a novel approach to example-based texture synthesis by using a robust deep learning process for creating tiles of arbitrary resolutions that resemble the structural components of an input texture. In this manner, our method is firstly much less memory limited owing to the fact that a new texture tile of small size is synthesized and merged with the original texture and secondly can easily produce missing parts of a large texture.