- We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets.
- In contrast to existing patch-based approaches, our deep-neural-network-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.
- Abstract: In the paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images.
- We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset.
- We finally demonstrate potential applications of our models in fine arts and forensic arts.
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@quantombone: “Sketch2Face: Deep Learning For Face Synthesis #computervision #Machinlearning #science”
[1606.03073] Convolutional Sketch Inversion