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The new approach is based on learning plausible face sketches from real face sketch images and finding the main aspects that approximate the input sketch. Treating input sketches as soft constraints allowed the researchers to produce high-quality face images with increased plausibility.
The new approach is said to include three core modules - CE (Component Embedding), FM (Feature Mapping), and IS (Image Synthesis). The first one adopts an auto-encoder architecture and separately learns five feature descriptors ( left-eye, right-eye, nose, mouth, and remainder). The other two modules develop deep learning sub-network for conditional image generation.
The team states that according to qualitative and quantitative evaluations, the method produces visually more pleasing face images compared to similar approaches. They also noted that the new approach is easy to use even if you are not an artist.
You can find the paper DeepFaceDrawing: Deep Generation of Face Images from Sketches prepared for SIGGRAPH 2020 here.