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  4. Portrait2Bust: DualStyleGAN-based portrait image stylization based on bust sculpture images
 
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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.
Author(s)
Sinha, Saptarshi Neil
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Weinmann, Michael
Delft University of Technology  
Mainwork
GCH 2023, Eurographics Workshop on Graphics and Cultural Heritage  
Project(s)
Perceptive Enhanced Realities of Colored collEctions through AI and Virtual Experiences  
Funder
European Commission  
Conference
Workshop on Graphics and Cultural Heritage 2023  
Open Access
File(s)
Download (5.58 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.2312/gch.20231159
10.24406/publica-2489
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Cultural und Creative Economy

  • Research Line: Computer graphics (CG)

  • Research Line: Computer vision (CV)

  • Research Line: Machine learning (ML)

  • LTA: Generation, capture, processing, and output of images and 3D models

  • 3D Computer graphics

  • Machine learning

  • Image manipulation

  • Conditional style transfer

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