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

arXiv: https://arxiv.org/abs/2009.00633 · HTML: https://mlsanity.com/paper/2009.00633/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data](https://mlsanity.com/paper/2202.03678/) (2022)
- [WISE: Whitebox Image Stylization by Example-based Learning](https://mlsanity.com/paper/2207.14606/) (2022)
- [Deep Generative Model based Rate-Distortion for Image Downscaling Assessment](https://mlsanity.com/paper/2403.15139/) (2024)
- [Look, Compare and Draw: Differential Query Transformer for Automatic Oil Painting](https://mlsanity.com/paper/2603.27720/) (2026)
- [Controlling Geometric Abstraction and Texture for Artistic Images](https://mlsanity.com/paper/2308.00148/) (2023)
- [Applying Medical Imaging Tractography Techniques to Painterly Rendering of Images](https://mlsanity.com/paper/2511.00702/) (2025)

## Abstract (arXiv)

Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, 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 neural style transfer) using the new benchmark dataset.