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- Title
Abstraction-perception preserving cartoon face synthesis.
- Authors
Ho, Sy-Tuyen; Huu, Manh-Khanh Ngo; Nguyen, Thanh-Danh; Phan, Nguyen; Nguyen, Vinh-Tiep; Ngo, Thanh Duc; Le, Duy-Dinh; Nguyen, Tam V.
- 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.
- Subjects
DIGITAL preservation; GENERATIVE adversarial networks
- Publication
Multimedia Tools & Applications, 2023, Vol 82, Issue 20, p31607
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-14853-9