Face sketch synthesis is the task of generating a sketch from an input face photo.
( Image credit: High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks )
In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions.
Ranked #1 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
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)
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
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.
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
FACE SKETCH SYNTHESIS IMAGE QUALITY ASSESSMENT IMAGE-TO-IMAGE TRANSLATION
In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data.
FACE SKETCH SYNTHESIS IMAGE-TO-IMAGE TRANSLATION STYLE TRANSFER
In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.