Semantic photo manipulation with a generative image prior
Name
2005.07727.pdf
Description
Accepted version
Size
3.99 MB
Format
Adobe PDF
Checksum (MD5)
e9b985bf61caff30012e528023cc107a
Author(s)
Bau, David
Strobelt, Hendrik
Peebles, William
Wulff, Jonas
Zhou, Bolei
Zhu, Jun-Yan
Torralba, Antonio
Date Issued
2019
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Version
Author's final manuscript
Abstract
© 2019 Association for Computing Machinery. All rights reserved. Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the highlevel attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
MIT-IBM Watson AI Lab
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3306346.3323023
