Paul L. Rosin, Yu-Kun Lai, David Mould, Ran Yi, Itamar Berger, Lars Doyle, Seungyong Lee, Chuan Li, Yong-Jin Liu, Amir Semmo, Ariel Shamir, Minjung Son, Holger Winnemöller
- Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of imageRecently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset.…
MetadatenAuthor details: | Paul L. Rosin, Yu-Kun Lai, David Mould, Ran Yi, Itamar Berger, Lars Doyle, Seungyong Lee, Chuan Li, Yong-Jin Liu, Amir SemmoORCiDGND, Ariel Shamir, Minjung Son, Holger Winnemöller |
---|
DOI: | https://doi.org/10.1007/s41095-021-0255-3 |
---|
ISSN: | 2096-0433 |
---|
ISSN: | 2096-0662 |
---|
Title of parent work (English): | Computational visual media |
---|
Publisher: | Springer Nature |
---|
Place of publishing: | London |
---|
Publication type: | Article |
---|
Language: | English |
---|
Date of first publication: | 2022/04/06 |
---|
Publication year: | 2022 |
---|
Release date: | 2024/06/25 |
---|
Tag: | benchmark; evaluation; image stylization; non-photorealistic rendering (NPR); portrait; style transfer |
---|
Volume: | 8 |
---|
Issue: | 3 |
---|
Number of pages: | 21 |
---|
First page: | 445 |
---|
Last Page: | 465 |
---|
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
---|
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
---|
Peer review: | Referiert |
---|
Publishing method: | Open Access / Gold Open-Access |
---|
| DOAJ gelistet |
---|
License (German): | CC-BY - Namensnennung 4.0 International |
---|