I've been learning about different generative neural networks and publications, trying to track down the work on top of which informative drawings has been built. A few of the papers I've been looking at include Pix2Pix, CycleGAN, Apple's S+U (Simulated + Unsupervised), NVIDIA's UNIT, CUT (and FastCut, and SingleCUT), which stands for contrastive learning for unpaired image-to-image translation, among others. I think there's potential to train these networks to translate line drawings to look like my hand sketches and viceversa.
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Pix2Pix. UC Berkeley. 2017. Image-to-Image Translation with Conditional Adversarial Nets.
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CycleGAN. UC Berkeley. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
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S+U. Apple. 2017. Learning from Simulated and Unsupervised Images through Adversarial Training
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UNIT. NVIDIA. 2017. Unsupervised Image-to-Image Translation Networks
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StyleGAN2. NVIDIA. March 2020. Analyzing and Improving the Image Quality of StyleGAN.
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CUT. UC Berkeley & Adobe Research. August 2020. Contrastive Learning for Unpaired Image-to-Image Translation.
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Informative Drawings. March 2022. Learning to generate line drawings that convey geometry and semantics.