@article{795,
  author = {Sy-Tuyen Ho and Manh-Khanh Huu and Thanh-Danh Nguyen and Nguyen Phan and Vinh-Tiep Nguyen and Thanh Ngo and Duy-Dinh Le and Tam Nguyen},
  title = {Abstraction-perception preserving cartoon face synthesis},
  abstract = {Portrait cartoonization aims at translating a portrait image to its cartoon version, which guarantees two conditions, namely, reducing textural details and synthesizing cartoon facial features (e.g., big eyes or line-drawing nose). To address this problem, we propose a two-stage training scheme based on GAN, which is powerful for stylization problems. The abstraction stage with a novel abstractive loss is used to reduce textural details. Meanwhile, the perception stage is adopted to synthesize cartoon facial features. To comprehensively evaluate the proposed method and other state-of-the-art methods for portrait cartoonization, we contribute a new challenging large-scale dataset named CartoonFace10K. In addition, we find that the popular metric FID focuses on the target style yet ignores the preservation of the input image content. We thus introduce a novel metric FISI, which compromises FID and SSIM to focus on both target features and retaining input content. Quantitative and qualitative results demonstrate that our proposed method outperforms other state-of-the-art methods.},
  year = {2023},
  journal = {Multimedia Tools and Applications},
  month = {03},
  issn = {1380-7501},
  url = {https://par.nsf.gov/biblio/10421008},
  doi = {10.1007/s11042-023-14853-9},
}