From Brandon Amos:
Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. There are many ways to do content-aware fill, image completion, and inpainting. In this blog post, I present Raymond Yeh and Chen Chen et al.’s paper “Semantic Image Inpainting with Perceptual and Contextual Losses,” which was just posted on arXiv on July 26, 2016.
Even if one doesn’t understand the details, the pictures are interesting enough!
Github repository: https://github.com/bamos/dcgan-completion.tensorflow
Blog post here: http://bamos.github.io/2016/08/09/deep-completion/