Yi, Ran, Liu, Yong-Jin, Lai, Yukun  ORCID: https://orcid.org/0000-0002-2094-5680 and Rosin, Paul ORCID: https://orcid.org/0000-0002-4965-3884
2019.
APDrawingGAN: Generating artistic portrait drawings from face photos with hierarchical GANs.
Presented at: CVPR 2019,
Long Beach, California, USA,
16-20 June 2019.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
IEEE,
pp. 10735-10744.
10.1109/CVPR.2019.01100
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Abstract
Significant progress has been made with image stylization using deep learning, especially with generative adversarial networks (GANs). However, existing methods fail to produce high quality artistic portrait drawings. Such drawings have a highly abstract style, containing a sparse set of continuous graphical elements such as lines, and so small artifacts are much more exposed than for painting styles. Moreover, artists tend to use different strategies to draw different facial features and the lines drawn are only loosely related to obvious image features. To address these challenges, we propose APDrawingGAN, a novel GAN based architecture that builds upon hierarchical generators and discriminators combining both a global network (for images as a whole) and local networks (for individual facial regions). This allows dedicated drawing strategies to be learned for different facial features. Since artists' drawings may not have lines perfectly aligned with image features, we develop a novel loss to measure similarity between generated and artists' drawings based on distance transforms, leading to improved strokes in portrait drawing. To train APDrawingGAN, we construct an artistic drawing dataset containing high-resolution portrait photos and corresponding professional artistic drawings. Extensive experiments, including a user study, show that APDrawingGAN produces significantly better artistic drawings than state-of-the-art methods.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 978-1-7281-3294-5 |
| ISSN: | 1063-6919 |
| Funders: | Royal Society |
| Projects: | NA150431 |
| Related URLs: | |
| Date of First Compliant Deposit: | 5 April 2019 |
| Last Modified: | 23 Jun 2026 10:09 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/121531 |
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