@inproceedings{10818fd12517423baee3cb97052b67a0,
title = "A Geometrically Aware Auto-Encoder for Multi-texture Synthesis",
abstract = "We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact and geometrically consistent latent space, where the texture representation and its spatial organisation are disentangled. Texture synthesis and interpolation tasks can be performed directly from these latent codes. Our experiments demonstrate that our model outperforms state-of-the-art feed-forward methods in terms of visual quality and various texture related metrics. The code is available online.",
keywords = "auto-encoder, scale/orientation models, texture synthesis",
author = "Pierrick Chatillon and Yann Gousseau and Sidonie Lefebvre",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023 ; Conference date: 21-05-2023 Through 25-05-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1007/978-3-031-31975-4\_20",
language = "English",
isbn = "9783031319747",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "263--275",
editor = "Luca Calatroni and Marco Donatelli and Serena Morigi and Marco Prato and Matteo Santacesaria",
booktitle = "Scale Space and Variational Methods in Computer Vision - 9th International Conference, SSVM 2023, Proceedings",
}