# Landmark Assisted CycleGAN for Cartoon Face Generation

arXiv: https://arxiv.org/abs/1907.01424 · HTML: https://mlsanity.com/paper/1907.01424/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation](https://mlsanity.com/paper/2107.06941/) (2021)
- [Generalizable Face Landmarking Guided by Conditional Face Warping](https://mlsanity.com/paper/2404.12322/) (2024)
- ['Hello, World!': Making GNNs Talk with LLMs](https://mlsanity.com/paper/2505.20742/) (2025)
- [F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networks](https://mlsanity.com/paper/2205.06204/) (2022)
- [PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN](https://mlsanity.com/paper/2306.13418/) (2023)

## Abstract (arXiv)

In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones. A major challenge of this task is that the structures of real and cartoon faces are in two different domains, whose appearance differs greatly from each other. Without explicit correspondence, it is difficult to generate a high quality cartoon face that captures the essential facial features of a person. In order to solve this problem, we propose landmark assisted CycleGAN, which utilizes face landmarks to define landmark consistency loss and to guide the training of local discriminator in CycleGAN. To enforce structural consistency in landmarks, we utilize the conditional generator and discriminator. Our approach is capable to generate high-quality cartoon faces even indistinguishable from those drawn by artists and largely improves state-of-the-art.