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

Abstract

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.

Generation of Reaction-Diffusion-Pattern-Like Images with Partially Variable Size

“…Studies on non-photorealistic rendering (NPR), which is a technique for computer graphics, been conducted since around 1990, and NPR methods for generating nonphotorealistic images of various expressions are currently being developed [1]- [6]. Non-photorealistic images are used in televisions, magazines, websites and social networking services for the purpose of improving visual effects.…”