Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Human-Like Sketches
Abstract
Image generation has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, when a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a human-like-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methods.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2019
- DOI:
- 10.48550/arXiv.1911.00426
- arXiv:
- arXiv:1911.00426
- Bibcode:
- 2019arXiv191100426X
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Accepted to Neurocomputing