Advancements In Computer Vision Models For View Synthesis: A Survey

In this post I survey a collection of Computer Vision Models that have made key advancements for View Synthesis. The fundamental idea behind View synthesis is the ability to take two-dimensional images, or videos, from different camera viewpoints and construct realistic novel views from them. Being able to synthesize a realistic novel view can depend on many factors such as, sufficient input images across various viewpoints and quality or resolution of the provided images. I will be only discussing models that have produced satisfactory results given their set of input and test images. Specifically, I have researched SRN (Scene Representation Networks), NeRF (Neural Radiance Fields), and NeuMan (Neural Human Radiance Field From a Single Video).