Shrivastava, GauravThis thesis investigates advanced techniques that are useful in video modeling for generation and enhancement tasks. In the first part of the thesis, we explore generative modeling that exploits the external corpus for learning priors. The task here is of video prediction, i.e., to extrapolate future sequences given a few context frames. In a followup work we also demonstrate how can we reduce the inference time further and make the video prediction model more efficient. Additionally, we demonstrate that we are not only able to extrapolate one future sequence from a given context frame but multiple sequences given context frames. In the second part, we explore the methods that exploit internal statistics of videos to perform various restoration and enhancement tasks. Here, we show how robustly they perform the restoration tasks like denoising, super-resolution, frame interpolation, and object removal tasks. Furthermore, in a follow-up work, we utilize the inherent compositionality of videos and internal statistics to perform a wider variety of enhancement tasks such as relighting, dehazing, and foreground/background manipulations. Lastly, we provide insight into our future work on how data-free enhancement techniques could be improved. Additionally, we provide further insights on how multisteps video prediction techniques can be improved.enDissertationArtificial intelligenceComputer scienceDiffusion ModelingGaussian ProcessGenerative AIVideo Enhancements TechniqueVideo Generation Technique