Face sketch synthesis is the task of generating a sketch from an input face photo.
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In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions.
Ranked #2 on Text-to-Image Generation on Multi-Modal-CelebA-HQ
We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature.
Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.
Ranked #1 on Face Sketch Synthesis on CUHK
However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches.
Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data.
Ranked #1 on Face Sketch Synthesis on CUFS (FID metric)
To this end, we propose a novel synthesis framework called Photo-Sketch Synthesis using Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution to high resolution images in an adversarial way.
Ranked #2 on Face Sketch Synthesis on CUHK