  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.
