English

ToonOut: Fine-tuned Background-Removal for Anime Characters

Computer Vision and Pattern Recognition 2025-09-09 v1 Machine Learning

Abstract

While state-of-the-art background removal models excel at realistic imagery, they frequently underperform in specialized domains such as anime-style content, where complex features like hair and transparency present unique challenges. To address this limitation, we collected and annotated a custom dataset of 1,228 high-quality anime images of characters and objects, and fine-tuned the open-sourced BiRefNet model on this dataset. This resulted in marked improvements in background removal accuracy for anime-style images, increasing from 95.3% to 99.5% for our newly introduced Pixel Accuracy metric. We are open-sourcing the code, the fine-tuned model weights, as well as the dataset at: https://github.com/MatteoKartoon/BiRefNet.

Keywords

Cite

@article{arxiv.2509.06839,
  title  = {ToonOut: Fine-tuned Background-Removal for Anime Characters},
  author = {Matteo Muratori and Joël Seytre},
  journal= {arXiv preprint arXiv:2509.06839},
  year   = {2025}
}