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Deep Tiling: Texture Tile Synthesis Using a Constant Space Deep Learning Approach

Vasilis Toulatzis, Ioannis Fudos:
ISVC 2021, LNCS Proceedings, pp 414-426

Texture expansion through deep tiling


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, mirroring or clamping 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 that attempt 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 existing texture and secondly can easily produce missing parts of a large texture.
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author = {Vasilis Toulatzis and
Ioannis Fudos},
editor = {George Bebis and
Vassilis Athitsos and
Tong Yan and
Manfred Lau and
Frederick Li and
Conglei Shi and
Xiaoru Yuan and
Christos Mousas and
Gerd Bruder},
title = {Deep Tiling: Texture Tile Synthesis Using a Constant Space Deep Learning
booktitle = {Advances in Visual Computing - 16th International Symposium, {ISVC}
2021, Virtual Event, October 4-6, 2021, Proceedings, Part {I}},
series = {Lecture Notes in Computer Science},
volume = {13017},
pages = {414--426},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-90439-5\_33},
doi = {10.1007/978-3-030-90439-5\_33}}


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