Multi-Density Sketch-to-Image Translation Network

Jialu HUANG, Jing LIAO, Zhifeng TAN, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

8 Citations (Scopus)

Abstract

Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as powerful tools in the art design industry. However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to micro structures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and generation of images. Moreover, our method has been successfully verified on various datasets for different applications including face editing, multi-modal sketchto- photo translation, and anime colorization, providing coarse-tofine levels of controls to these applications.
Original languageEnglish
Pages (from-to)4002-4015
JournalIEEE Transactions on Multimedia
Volume24
Early online date14 Sept 2021
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Deep image synthesis
  • GAN
  • interactive editing
  • multi-scale disentangle

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