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.

Accepted to Neurocomputing

References in corpus (8)

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
• DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
• InstaGAN: Instance-aware Image-to-Image Translation
• Photographic Image Synthesis with Cascaded Refinement Networks
• SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
• CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation