Scraping Textures from Natural Images for Synthesis and Editing

Xueting Li, Xiaolong Wang, Ming Hsuan Yang, Alexei A. Efros, Sifei Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Existing texture synthesis methods focus on generating large texture images given a small texture sample. But such samples are typically assumed to be highly curated: rectangular, clean, and stationary. This paper aims to scrape textures directly from natural images of everyday objects and scenes, build texture models, and employ them for texture synthesis, texture editing, etc. The key idea is to jointly learn image grouping and texture modeling. The image grouping module discovers clean texture segments, each of which is represented as a texture code and a parametric sine wave by the texture modeling module. By enforcing the model to reconstruct the input image from the texture codes and sine waves, our model can be learned via self-supervision on a set of cluttered natural images, without requiring any form of annotation or clean texture images. We show that the learned texture features capture many natural and man-made textures in real images, and can be applied to tasks like texture synthesis, texture editing and texture swapping.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783031197833
Publication statusPublished - 2022
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: 2022 Oct 232022 Oct 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th European Conference on Computer Vision, ECCV 2022
CityTel Aviv

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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