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2023
Conference Paper
Title
Portrait2Bust: DualStyleGAN-based portrait image stylization based on bust sculpture images
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.
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.
Author(s)
Open Access
File(s)
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Keyword(s)