DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation
Abstract
In this work, we consider the task of generating highly-realistic 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.
Cite
Text
Ganin et al. "DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46475-6_20Markdown
[Ganin et al. "DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/ganin2016eccv-deepwarp/) doi:10.1007/978-3-319-46475-6_20BibTeX
@inproceedings{ganin2016eccv-deepwarp,
title = {{DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation}},
author = {Ganin, Yaroslav and Kononenko, Daniil and Sungatullina, Diana and Lempitsky, Victor S.},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {311-326},
doi = {10.1007/978-3-319-46475-6_20},
url = {https://mlanthology.org/eccv/2016/ganin2016eccv-deepwarp/}
}