Journal
NEUROCOMPUTING
Volume 460, Issue -, Pages 256-265Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2021.07.029
Keywords
Face sketch-to-photo synthesis; Image translation; Neural network; Generative adversarial network
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Funding
- Major Research Plan of National Natural Science Foundation of China [61991451]
- Shenzhen special fund for the strategic development of emerging industries [ZDYBH201900000002]
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This paper introduces a method called Cali-Sketch for generating photo-realistic images from human-like sketches, which successfully addresses the generation problem of realistic face images from sketches through a combination of stroke calibration network and image synthesis network.
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. (c) 2021 Elsevier B.V. All rights reserved.
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