The tool also allows users to add a style filter, changing a generated image to adapt the style of a particular painter, or change a daytime scene to sunset.
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Trained on real images, the discriminator coaches the generator with pixel-by-pixel feedback on how to improve the realism of its synthetic images.Īfter training on a million real images, the discriminator knows that real ponds and lakes contain reflections - so the generator learns to create a convincing imitation. The generator creates images that it presents to the discriminator.
“And then the neural network is able to fill in all of the detail and texture, and the reflections, shadows and colors, based on what it has learned about real images.”ĭespite lacking an understanding of the physical world, GANs can produce convincing results because of their structure as a cooperating pair of networks: a generator and a discriminator. “It’s like a coloring book picture that describes where a tree is, where the sun is, where the sky is,” Catanzaro said. Swap a segment label from “grass” to “snow” and the entire image changes to a winter scene, with a formerly leafy tree turning barren. GauGAN allows users to draw their own segmentation maps and manipulate the scene, labeling each segment with labels like sand, sky, sea or snow.ĭeveloped using the PyTorch deep learning framework, the AI model then fills in the landscape with show-stopping results: Draw in a pond, and nearby elements like trees and rocks will appear as reflections in the water. GauGAN is now available as a desktop application, called NVIDIA Canvas.Ĭatanzaro likens the technology behind GauGAN to a “smart paintbrush” that can fill in the details inside rough segmentation maps, the high-level outlines that show the location of objects in a scene. “It’s much easier to brainstorm designs with simple sketches, and this technology is able to convert sketches into highly realistic images,” said Bryan Catanzaro, vice president of applied deep learning research at NVIDIA.
With an AI that understands how the real world looks, these professionals could better prototype ideas and make rapid changes to a synthetic scene. GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers. (Photo from Wikimedia Commons, licensed under public domain). Post-Impressionist Paul Gauguin painted several self-portraits, including this 1885 work housed at the Kimbell Art Museum.
The interactive app using the model, in a lighthearted nod to the post-Impressionist painter, has been christened GauGAN. The tool leverages generative adversarial networks, or GANs, to convert segmentation maps into lifelike images. A novice painter might set brush to canvas aiming to create a stunning sunset landscape - craggy, snow-covered peaks reflected in a glassy lake - only to end up with something that looks more like a multi-colored inkblot.īut a deep learning model developed by NVIDIA Research can do just the opposite: it turns rough doodles into photorealistic masterpieces with breathtaking ease.