DeepFaceDrawing Generates Photorealistic Portraits from Freehand Sketches - Synced

A team of researchers from the Chinese Academy of Sciences and the City University of Hong Kong has introduced a local-to-global approach that can generate lifelike human portraits from relatively rudimentary sketches. Recent deep image-to-image translation techniques have enabled the prompt generation of human face images from sketches, but these methods tend to suffer from overfitting to their inputs. They thus achieve the most realistic results only when the source drawings have high-quality artistry or are accompanied by edge maps. Unlike most deep learning based solutions for sketch-to-image translation that take input sketches as fixed, 'hard' constraints and then attempt to reconstruct the missing texture or shading information between strokes, the key idea behind the new approach is to implicitly learn a space of plausible face sketches from real face sketch images and find the point in this space that best approximates the input sketch. Because this approach treats input sketches more as'soft' constraints that will guide image synthesis, it is able to produce high-quality face images with increased plausibility even from rough and/or incomplete inputs.