# Towards the Automatic Anime Characters Creation with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1708.05509 · HTML: https://mlsanity.com/paper/1708.05509/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2017.

## Built on by (most cited first, 29 of 29)

- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration](https://mlsanity.com/paper/2304.02419/) (2023)
- [UDE: A Unified Driving Engine for Human Motion Generation](https://mlsanity.com/paper/2211.16016/) (2022)
- [Deep Animation Video Interpolation in the Wild](https://mlsanity.com/paper/2104.02495/) (2021)
- [Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images](https://mlsanity.com/paper/2106.07873/) (2021)
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- [X-Dancer: Expressive Music to Human Dance Video Generation](https://mlsanity.com/paper/2502.17414/) (2025)
- [Facial-Sketch Synthesis: A New Challenge](https://mlsanity.com/paper/2112.15439/) (2021)
- [Bidirectional Autoregressive Diffusion Model for Dance Generation](https://mlsanity.com/paper/2402.04356/) (2024)
- [Surrogate Gradient Field for Latent Space Manipulation](https://mlsanity.com/paper/2104.09065/) (2021)
- [DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition](https://mlsanity.com/paper/2101.08674/) (2021)
- [Collaborative Neural Rendering using Anime Character Sheets](https://mlsanity.com/paper/2207.05378/) (2022)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models](https://mlsanity.com/paper/2310.09912/) (2023)
- [An adaptive artificial neural network-based generative design method for layout designs](https://mlsanity.com/paper/2101.12410/) (2021)
- [Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection](https://mlsanity.com/paper/2403.13479/) (2024)
- [DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training](https://mlsanity.com/paper/2101.07524/) (2021)
- [Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning](https://mlsanity.com/paper/2102.12139/) (2021)
- [Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance](https://mlsanity.com/paper/2308.16725/) (2023)
- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [Controlled GAN-Based Creature Synthesis via a Challenging Game Art Dataset -- Addressing the Noise-Latent Trade-Off](https://mlsanity.com/paper/2108.08922/) (2021)
- [Metamobility: Connecting Future Mobility with Metaverse](https://mlsanity.com/paper/2301.06991/) (2023)
- [Systematic Analysis and Removal of Circular Artifacts for StyleGAN](https://mlsanity.com/paper/2103.01090/) (2021)
- [Level generation and style enhancement -- deep learning for game development overview](https://mlsanity.com/paper/2107.07397/) (2021)
- [Self-supervised Enhancement of Latent Discovery in GANs](https://mlsanity.com/paper/2112.08835/) (2021)
- [Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport](https://mlsanity.com/paper/2204.08472/) (2022)
- [Theoretical Insights into CycleGAN: Analyzing Approximation and Estimation Errors in Unpaired Data Generation](https://mlsanity.com/paper/2407.11678/) (2024)
- [Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks](https://mlsanity.com/paper/2105.07621/) (2021)
- [se-Shweshwe Inspired Fashion Generation](https://mlsanity.com/paper/2203.00435/) (2022)

## Abstract (arXiv)

Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result. In this work, we explore the training of GAN models specialized on an anime facial image dataset. We address the issue from both the data and the model aspect, by collecting a more clean, well-suited dataset and leverage proper, empirical application of DRAGAN. With quantitative analysis and case studies we demonstrate that our efforts lead to a stable and high-quality model. Moreover, to assist people with anime character design, we build a website (http://make.girls.moe) with our pre-trained model available online, which makes the model easily accessible to general public.