ChunkyGAN: Real Image Inversion via Segments

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00360787" target="_blank" >RIV/68407700:21230/22:00360787 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-20050-2_12" target="_blank" >https://doi.org/10.1007/978-3-031-20050-2_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-20050-2_12" target="_blank" >10.1007/978-3-031-20050-2_12</a>

Result language

angličtina

Original language name

ChunkyGAN: Real Image Inversion via Segments

Original language description

We present ChunkyGAN—a novel paradigm for modeling and editing images using generative adversarial networks. Unlike previous techniques seeking a global latent representation of the input image, our approach subdivides the input image into a set of smaller components (chunks) specified either manually or automatically using a pre-trained segmentation network. For each chunk, the latent code of a generative network is estimated locally with greater accuracy thanks to a smaller number of constraints. Moreover, during the optimization of latent codes, segmentation can further be refined to improve matching quality. This process enables high-quality projection of the original image with spatial disentanglement that previous methods would find challenging to achieve. To demonstrate the advantage of our approach, we evaluated it quantitatively and also qualitatively in various image editing scenarios that benefit from the higher reconstruction quality and local nature of the approach. Our method is flexible enough to manipulate even out-of-domain images that would be hard to reconstruct using global techniques.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computer Vision – ECCV 2022, Part XXIII

ISBN

978-3-031-20049-6

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

16

Pages from-to

189-204

Publisher name

Springer

Place of publication

Cham

Event location

Tel Aviv

Event date

Oct 23, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000904146300012