Structural consistency and controllability for diverse colorization

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

Abstract

Colorizing a given gray-level image is an important task in the media and advertising industry. Due to the ambiguity inherent to colorization (many shades are often plausible), recent approaches started to explicitly model diversity. However, one of the most obvious artifacts, structural inconsistency, is rarely considered by existing methods which predict chrominance independently for every pixel. To address this issue, we develop a conditional random field based variational auto-encoder formulation which is able to achieve diversity while taking into account structural consistency. Moreover, we introduce a controllability mechanism that can incorporate external constraints from diverse sources including a user interface. Compared to existing baselines, we demonstrate that our method obtains more diverse and globally consistent colorizations on the LFW, LSUN-Church and ILSVRC-2015 datasets.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsMartial Hebert, Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu
PublisherSpringer-Verlag Berlin Heidelberg
Pages603-619
Number of pages17
ISBN (Print)9783030012304
DOIs
StatePublished - Jan 1 2018
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: Sep 8 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11210 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Computer Vision, ECCV 2018
CountryGermany
CityMunich
Period9/8/189/14/18

Keywords

  • Colorization
  • Gaussian-Conditional Random Field
  • VAE

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Messaoud, S., Forsyth, D., & Schwing, A. G. (2018). Structural consistency and controllability for diverse colorization. In M. Hebert, Y. Weiss, V. Ferrari, & C. Sminchisescu (Eds.), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings (pp. 603-619). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11210 LNCS). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-030-01231-1_37