4 Cutting-Edge AI Techniques for Video Generation

It is no secret that algorithms today can generate very realistic deepfakes – images or videos that are totally fake but very hard to distinguish from the real ones. You can make Mark Zuckerberg talking about "one man with total control of billions of people stolen data" and the suspicion will come only because Mark is not likely to say these exact words while the video itself looks very realistic. So, let's see what are some of the state-of-the-art approaches to video generation. If these summaries of scientific AI research papers are useful for you, you can subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries. If you'd like to skip around, here are the papers we featured: We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature.