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NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraits

Rosin, Paul L. ORCID:, Lai, Yu-Kun ORCID:, Mould, David, Yi, Ran, Berger, Itamar, Doyle, Lars, Lee, Seungyong, Li, Chuan, Liu, Yong-Jin, Semmo, Amir, Shamir, Ariel, Son, Minjung and Winnemöller, Holger 2022. NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraits. Computational Visual Media 8 , pp. 445-465. 10.1007/s41095-021-0255-3

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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 image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher: SpringerOpen
ISSN: 2096-0433
Date of First Compliant Deposit: 8 April 2022
Date of Acceptance: 16 September 2021
Last Modified: 06 May 2023 03:20

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