DeepWarp: Photorealistic image resynthesis for gaze manipulation

Yaroslav Ganin, Daniil Kononenko, Diana Sungatullina, Victor Lempitsky

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    55 Citations (Scopus)


    In this work, we consider the task of generating highlyrealistic images of a given face with a redirected gaze. We treat this problem as a specific instance of conditional image generation and suggest a new deep architecture that can handle this task very well as revealed by numerical comparison with prior art and a user study. Our deep architecture performs coarse-to-fine warping with an additional intensity correction of individual pixels. All these operations are performed in a feed-forward manner, and the parameters associated with different operations are learned jointly in the end-to-end fashion. After learning, the resulting neural network can synthesize images with manipulated gaze, while the redirection angle can be selected arbitrarily from a certain range and provided as an input to the network.

    Original languageEnglish
    Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
    EditorsBastian Leibe, Nicu Sebe, Max Welling, Jiri Matas
    PublisherSpringer Verlag
    Number of pages16
    ISBN (Print)9783319464749
    Publication statusPublished - 2016
    Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
    Duration: 8 Oct 201616 Oct 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9906 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference14th European Conference on Computer Vision, ECCV 2016


    • Deep learning
    • Gaze correction
    • Spatial transformers
    • Supervised learning
    • Warping


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