Pre-Publication V2. A technique that uses deep neural networks to generate learn implicit and hybrid scene representations for synthesizing photorealistic images and video from learned representations of a scene, rather than solely traditional geometric-based rendering methods. pipelines. It combines data-driven methods with physically motivated image formation to enable effects like anisotropy, global illumination, and transparency, producing photorealistic images and videos. It can reproduce complex visual properties including reflections, transparency, and global illumination and serves as the foundational methodology underlying related techniques including Neural Radiance Fields (NeRFs) and neural avatars.
Deliberation Summary:
(1) Consider a separate term for material that is part captured and part generated, where existing data is used to infer additions to an image, a shot, or a recording. (2) Simplify the language especially with regard to the capability claimed for the technique.
Pre-Publication V1. A technique that uses deep neural networks to learn implicit and hybrid scene representations for synthesizing photorealistic images and video from learned representations of a scene, rather than traditional geometric pipelines. It combines data-driven methods with physically motivated image formation to enable effects like anisotropy, global illumination, and transparency, producing photorealistic images and videos. It can reproduce complex visual properties including reflections, transparency, and global illumination and serves as the foundational methodology underlying related techniques including Neural Radiance Fields (NeRFs) and neural avatars.