Barbershop: GAN-based Image Compositing using Segmentation Masks
Barbershop: GAN-based Image Compositing using Segmentation Masks
Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka
arXiv | BibTeX | Project Page | Video
Abstract Seamlessly blending features from multiple images is extremely challenging because of complex relationships in lighting, geometry, and partial occlusion which cause coupling between different parts of the image. Even though recent work on GANs enables synthesis of realistic hair or faces, it remains difficult to combine them into a single, coherent, and plausible image rather than a disjointed set of image patches. We present a novel solution to image blending, particularly for the problem of hairstyle transfer, based on GAN-inversion. We propose a novel latent space for image blending which is better at preserving detail and encoding spatial information, and propose a new GAN-embedding algorithm which is able to slightly modify images to conform to a common segmentation mask. Our novel representation enables the transfer of the visual properties from multiple reference images including specific details such as moles and wrinkles, and because we do image blending in a latent-space we are able to synthesize images that are coherent. Our approach avoids blending artifacts present in other approaches and finds a globally consistent image. Our results demonstrate a significant improvement over the current state of the art in a user study, with users preferring our blending solution over 95 percent of the time.
Description
Official Implementation of Barbershop.
Updates
2/6/2021 Add project page.
BibTeX
@misc{zhu2021barbershop,
title={Barbershop: GAN-based Image Compositing using Segmentation Masks},
author={Peihao Zhu and Rameen Abdal and John Femiani and Peter Wonka},
year={2021},
eprint={2106.01505},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
42 Dec 27, 2022
150 Dec 7, 2022
33 Dec 18, 2022
30 Dec 24, 2022
191 Dec 31, 2022
2 Jan 18, 2022
77 Jan 5, 2023
18 Dec 23, 2022
305 Dec 16, 2022
258 Jan 2, 2023
249 Jan 8, 2023
118 Dec 27, 2022
54 Dec 6, 2022
49 Oct 31, 2022
61 Nov 27, 2022
23 Dec 9, 2022
203 Jan 5, 2023
194 Dec 6, 2022