A deep-learning tool that lets you clone an artistic style onto a photo
May 10, 2017
“Deep Photo Style Transfer” is a cool new artificial-intelligence image-editing software tool that lets you transfer a style from another (“reference”) photo onto your own photo, as shown in the above examples.
An open-access arXiv paper by Cornell University computer scientists and Adobe collaborators explains that the tool can transpose the look of one photo (such as the time of day, weather, season, and artistic effects) onto your photo, making it reminiscent of a painting, but that is still photorealistic.
“What motivated us is the idea that style could be imprinted on a photograph, but it is still intrinsically the same photo, said Cornell computer science professor Kavita Bala. “This turned out to be incredibly hard. The key insight finally was about preserving boundaries and edges while still transferring the style.”
To do that, the researchers created deep-learning software that can add a neural network layer that pays close attention to edges within the image, like the border between a tree and a lake.
The software is still in the research stage.
Bala, Cornell doctoral student Fujun Luan, and Adobe collaborators Sylvian Paris and Eli Shechtman will present their paper at the Conference on Computer Vision and Pattern Recognition on July 21–26 in Honolulu.
This research is supported by a Google Faculty Re-search Award and NSF awards.
Abstract of Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.