# CariGANs: Unpaired Photo-to-Caricature Translation

arXiv: https://arxiv.org/abs/1811.00222 · HTML: https://mlsanity.com/paper/1811.00222/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 11 of 11)

- [Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer](https://mlsanity.com/paper/2203.13248/) (2022)
- [BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation](https://mlsanity.com/paper/2110.11728/) (2021)
- [Rewriting Geometric Rules of a GAN](https://mlsanity.com/paper/2207.14288/) (2022)
- [Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects](https://mlsanity.com/paper/2311.02332/) (2023)
- [Transfer Learning for Pose Estimation of Illustrated Characters](https://mlsanity.com/paper/2108.01819/) (2021)
- [AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging](https://mlsanity.com/paper/2211.07818/) (2022)
- [Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps](https://mlsanity.com/paper/2206.02903/) (2022)
- [Tag-based annotation creates better avatars](https://mlsanity.com/paper/2302.07354/) (2023)
- [Few-shots Portrait Generation with Style Enhancement and Identity Preservation](https://mlsanity.com/paper/2303.00377/) (2023)
- [Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation](https://mlsanity.com/paper/2207.08072/) (2022)
- [Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes](https://mlsanity.com/paper/2211.02141/) (2022)

## Abstract (arXiv)

Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transformation from face photos to caricatures, and CariStyGAN, which transfers the style appearance from caricatures to face photos without any geometry deformation. In this way, a difficult cross-domain translation problem is decoupled into two easier tasks. The perceptual study shows that caricatures generated by our CariGANs are closer to the hand-drawn ones, and at the same time better persevere the identity, compared to state-of-the-art methods. Moreover, our CariGANs allow users to control the shape exaggeration degree and change the color/texture style by tuning the parameters or giving an example caricature.