This paper describes the summary of NerfDiff, a novel AI framework designed to enable high-quality and consistent multiple views from a single input image.
It uses a combination of a neural ray field and a 3D-aware conditional diffusion model to achieve this result.
The challenge, according to the authors, is in resolving the uncertainty associated with large unseen regions in the novel views.
NeRF has been used to generate high-quality views in the past.
This paper presents a new approach for training neural ray fields that allows for rendering views close to the scene that are subject to great variations due to their sensitivity to light microscopically small areas.
They claim that this approach can be useful for applications such as AR/AR gaming
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