GazeFlow: Gaze Redirection with Normalizing Flows

Yong Wu, Hanbang Liang, Xianxu Hou, Linlin Shen*

*Corresponding author for this work

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

1 Citation (Scopus)


Gaze estimation often requires a large scale datasets with well annotated gaze information to train the estimator. However, such a dataset requires costive annotation and is usually very difficult to collect. Therefore, a number of gaze redirection approaches have been proposed to address such a problem. However, existing methods lack the ability to precisely synthesize images with target gaze and head pose in complex lighting scenes. As a powerful technique to model the distribution of given data, normalizing flows have the ability to generate photo-realistic images and provide flexible latent space manipulation. In this work, we present a novel flow-based generative model, GazeFlow11The code will be made available at, for gaze redirection. The visual results of gaze redirection show that the quality of eye images synthesized by GazeFlow is significantly higher than that of other approaches like Deep Warp and PRGAN. Our approach has also been applied to augment the training data to improve the accuracy of gaze estimators and significant improvement has been achieved for both within dataset and cross dataset experiments.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
Publication statusPublished - 18 Jul 2021
Externally publishedYes
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 18 Jul 202122 Jul 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
CityVirtual, Shenzhen


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