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Despite the recent upsurge of activity in image-based non-photorealisticrendering (NPR), and in particular portrait image stylisation, due to theadvent of neural style transfer, the state of performance evaluation in thisfield is limited, especially compared to the norms in the computer vision andmachine learning communities. Unfortunately, the task of evaluating imagestylisation is thus far not well defined, since it involves subjective,perceptual and aesthetic aspects. To make progress towards a solution, thispaper proposes a new structured, three level, benchmark dataset for theevaluation of stylised portrait images. Rigorous criteria were used for itsconstruction, and its consistency was validated by user studies. Moreover, anew methodology has been developed for evaluating portrait stylisationalgorithms, which makes use of the different benchmark levels as well asannotations provided by user studies regarding the characteristics of thefaces. We perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose, and also both traditional NPRapproaches and neural style transfer) using the new benchmark dataset.

NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits