Google research scientists report that they have produced realistic frames from open source video data sets at scale. Neural networks are able to generate complete videos from only a start and end frame, but it’s the complexity, information density and randomness of video that have made it too challenging to create such realistic clips at scale. The scientists wrote that, to their knowledge, “this is the first promising application of video-generation models to videos of this complexity.” The systems are based on a neural architecture known as Transformers, as described in a Google Brain paper, and are autoregressive, “meaning they generate videos pixel by pixel.” Continue reading Google Scientists Generate Realistic Videos at Scale with AI

Google researchers have created a machine learning system that adds color to black & white videos, and can also choose which specific objects, people and pets receive the color treatment. The technology is based on what’s called a convolutional neural network, which is architecturally suited for object tracking and video stabilization. Meanwhile, Nvidia has debuted an algorithm that slows down video, without the jitters, after it’s been captured, by using a neural network to create “in between” frames required for smooth motion. Continue reading Google, Nvidia Train Neural Networks to Post-Process Video