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Cali-sketch: Stroke calibration and completion for high-quality face image generation from human-like sketches.
- Source :
-
Neurocomputing . Oct2021, Vol. 460, p256-265. 10p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FACE
*GENERATIVE adversarial networks
*CALIBRATION
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 460
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 152273530
- Full Text :
- https://doi.org/10.1016/j.neucom.2021.07.029