Two Minute Papers: NVIDIA’s AI Removes Objects From Your Photos | Two Minute Papers #255
These fellows colors, this is two-minute papers with Kato Ejona Ifehir. Ever had an experience when you shot an almost perfect photograph of, for instance, an amazing landscape, but unfortunately, it was littered with unwanted objects. If only we had an algorithm that could perform image impainting, in other words, delete a small part of an image and have it automatically filled in. So let’s have a look at MVDS AI-based solution. On the left, you see the white regions that are given to the algorithm to correct, and on the right, you see the corrected images. So, it works amazingly well, but the question is, why? This is an established research field, so what new can an AI-based approach bring to the table? Well, traditional non-learning approaches either try to fill these holes in with other pixels from the same image that have similar neighborhoods, copy-paste something similar, if you will, or they try to record the distribution of pixel colors, and try to fill in something using that knowledge. And here comes the important part. None of these traditional approaches have an intuitive understanding of the contents of the image, and that is the main value proposition of the neural network-based learning techniques. This work also borrows from earlier artistic style transfer methods to make sure that not only the content, but the style of the impainted regions also match the original image. It is also remarkable that this new method works with images that are devoid of symmetries and can also deal with cases where we cut out really crazy irregularly-shaped holes. Of course, like every good piece of research work, it has to be compared to previous algorithms. As you can see here, the quality of different techniques is measured against a reference output, and it is quite clear that this method produces more convincing results than its competitors. For reference, PatchMatch is a landmark paper from almost 10 years ago that still represents the state of the art for non-learning-based techniques. The paper contains a ton more of these comparisons, so make sure to have a look. Without doubt, this is going to be an invaluable tool for artists in the future. In fact, in this very series, we use Photoshop’s built-in image-impaining tool on a daily basis, so this will make our lives much easier. Loving it. Also, did you know that you can get early access to each of these videos? If you are addicted to the series, have a look at our Patreon page, Patreon.com slash 2-minute papers, or just click the link in the video description. There are also other, really cool perks, like getting your name as a key supporter in the video description, or deciding the order of the next few episodes. We also support Cryptocurrencies, the addresses are in the video description, and with this, you also help us make better videos in the future. Thanks for watching and for your generous support, and I’ll see you next time.
