Super resolution in images and video is a complex task that represents an array of different perceptual abilities, from object recognition to movement flow recognition. SinGAN’s architecture showed that SOTA super resolution from a single training image (without priors) is possible. TSR is an architecture that performs temporal super resolution on videos, that showed SOTA performance on a single training video. In this project we tried modifying SinGAN’s architecture and explore its ability to generalize its super resolution capabilities to 3D data – videos, the main difference from TSR’s architecture is our usage of GANs and the adversarial training scheme. To achieve this, we expanded SinGAN’s architecture to support temporal-spatial patches and optimized the architecture and hyper parameters. The results are compared to the SOTA solution (TSR) using different metrics.