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Single image 3D face reconstruction with accurate geometric details is a critical and challenging task due to the similar appearance on the face surface and fine details in organs. In this work, we introduce a self-supervised 3D face reconstruction approach from a single image that can recover detailed textures under different camera settings. The proposed network learns high-quality disparity maps from stereo face images during the training stage, while just a single face image is required to generate the 3D model in real applications. To recover fine details of each organ and facial surface, the framework introduces facial landmark spatial consistency to constrain the face recovering learning process in local point level and segmentation scheme on facial organs to constrain the correspondences at the organ level. The face shape and textures will further be refined by establishing holistic constraints based on the varying light illumination and shading information. The proposed learning framework can recover more accurate 3D facial details both quantitatively and qualitatively compared with state-of-the-art 3DMM and geometry-based reconstruction algorithms based on a single image.
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Abstraction-perception preserving cartoon face synthesis
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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- Award ID(s):
- 2025234
- PAR ID:
- 10421008
- Date Published:
- Journal Name:
- Multimedia Tools and Applications
- ISSN:
- 1380-7501
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11042-023-14853-9
