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Abstraction-perception preserving cartoon face synthesis

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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.

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Data Availability

Data available on request from the authors

Notes

  1. https://github.com/nagadomi/lbpcascade_animeface

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the GPU donation.

Funding

This research is funded by Vietnam National University Ho Chi Minh City (VNUHCM) under grant number C2022-26-01. This work is also supported by the National Science Foundation (NSF) under Grant 2025234. Thanh-Danh Nguyen is funded by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2022.ThS.104.

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Authors and Affiliations

  1. University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Sy-Tuyen Ho, Manh-Khanh Ngo Huu, Thanh-Danh Nguyen, Nguyen Phan, Vinh-Tiep Nguyen, Thanh Duc Ngo & Duy-Dinh Le

  2. University of Dayton, Dayton, OH, USA

    Tam V. Nguyen

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  1. Sy-Tuyen Ho
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Correspondence to Tam V. Nguyen.

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Ho, ST., Huu, MK.N., Nguyen, TD. et al. Abstraction-perception preserving cartoon face synthesis. Multimed Tools Appl 82, 31607–31624 (2023). https://doi.org/10.1007/s11042-023-14853-9

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