Portrait2Bust: DualStyleGAN-based Portrait Image Stylization Based on Bust Sculpture Images
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In cultural heritage, portrait paintings and busts are special genres of artworks which are used to show the appearance and expression of a human subject. The purpose of such artwork is to serve as remembrance of the person who is depicted in that portrait or bust. The bust can moreover serve as a 3D representation of a portrait painting. Therefore, it would be interesting to stylize a portrait painting based on a specific bust, i.e. the generation of a 2D image of a bust corresponding to the person depicted in the portrait image. In this paper, we analyze and discuss the stylization of portrait paintings and photographs of human faces with busts using a deep learning based style transfer approach. To capture the characteristics in the appearance of busts, we created a novel dataset of busts and used DualStyleGAN for the use cases of stylizing portrait paintings and stylizing human faces based on our novel bust style. Our experiments show the potential of this approach. Stylizing human faces as busts might not only be appealing to experts that might save time and effort for generating an initial stylization to refine later on, but also increase the engagement of novice users and exhibition visitors with cultural heritage.
Description
CCS Concepts: Computing methodologies → Machine learning; Image manipulation; Computer graphics; Applied computing → Arts and humanities
@inproceedings{10.2312:gch.20231159,
booktitle = {Eurographics Workshop on Graphics and Cultural Heritage},
editor = {Bucciero, Alberto and Fanini, Bruno and Graf, Holger and Pescarin, Sofia and Rizvic, Selma},
title = {{Portrait2Bust: DualStyleGAN-based Portrait Image Stylization Based on Bust Sculpture Images}},
author = {Sinha, Saptarshi Neil and Weinmann, Michael},
year = {2023},
publisher = {The Eurographics Association},
ISSN = {2312-6124},
ISBN = {978-3-03868-217-2},
DOI = {10.2312/gch.20231159}
}