# WarpGAN: Automatic Caricature Generation

arXiv: https://arxiv.org/abs/1811.10100 · HTML: https://mlsanity.com/paper/1811.10100/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 28 of 28)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [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)
- [AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation](https://mlsanity.com/paper/2102.12593/) (2021)
- [Rewriting Geometric Rules of a GAN](https://mlsanity.com/paper/2207.14288/) (2022)
- [Exemplar-Based 3D Portrait Stylization](https://mlsanity.com/paper/2104.14559/) (2021)
- [3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction](https://mlsanity.com/paper/2103.08204/) (2021)
- [Deformable One-shot Face Stylization via DINO Semantic Guidance](https://mlsanity.com/paper/2403.00459/) (2024)
- [DemoCaricature: Democratising Caricature Generation with a Rough Sketch](https://mlsanity.com/paper/2312.04364/) (2023)
- [StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation](https://mlsanity.com/paper/2107.04331/) (2021)
- [Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation](https://mlsanity.com/paper/2203.12835/) (2022)
- [DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields](https://mlsanity.com/paper/2309.04410/) (2023)
- [A Domain Gap Aware Generative Adversarial Network for Multi-domain Image Translation](https://mlsanity.com/paper/2110.10837/) (2021)
- [DCT-Net: Domain-Calibrated Translation for Portrait Stylization](https://mlsanity.com/paper/2207.02426/) (2022)
- [StyO: Stylize Your Face in Only One-shot](https://mlsanity.com/paper/2303.03231/) (2023)
- [Generalizable Face Landmarking Guided by Conditional Face Warping](https://mlsanity.com/paper/2404.12322/) (2024)
- [Fix the Noise: Disentangling Source Feature for Controllable Domain Translation](https://mlsanity.com/paper/2303.11545/) (2023)
- [Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps](https://mlsanity.com/paper/2206.02903/) (2022)
- [Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation](https://mlsanity.com/paper/2307.13978/) (2023)
- [Hide and Seek: How Does Watermarking Impact Face Recognition?](https://mlsanity.com/paper/2404.18890/) (2024)
- [MODIFY: Model-driven Face Stylization without Style Images](https://mlsanity.com/paper/2303.09831/) (2023)
- [StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition](https://mlsanity.com/paper/2604.21689/) (2026)
- [Non-Parametric Neural Style Transfer](https://mlsanity.com/paper/2108.12847/) (2021)
- [3D Face Arbitrary Style Transfer](https://mlsanity.com/paper/2303.07709/) (2023)
- [FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer](https://mlsanity.com/paper/2307.09020/) (2023)
- [High-Quality Face Caricature via Style Translation](https://mlsanity.com/paper/2311.13338/) (2023)
- [Neural Image Space Tessellation effect](https://mlsanity.com/paper/2602.23754/) (2026)
- [CaricHarmony: Contrastive Diffusion Paths for Identity-Preserving Caricature Synthesis](https://mlsanity.com/paper/2606.13964/) (2026)

## Abstract (arXiv)

We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating a diverse set of caricatures while preserving the identities. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated.