Neural Graphics Primitive – Meta Human in Unreal Engine Using NeRF Neural Radiance Field

The advent of NeRF, similar to photogrammetry, has sparked debates on how best to define and elucidate its concepts for easier comprehension. While both techniques are utilized to construct realistic scenes, NeRF distinguishes itself by aiming to craft volumetric representations and generate high-resolution photorealistic views of actual objects and environments. Unlike photogrammetry, which relies on multiple overlapping images taken from various angles, NeRF quickly learns a 3D scene and renders it almost instantly. Although some degree of image overlap is still necessary, NeRF employs artificial intelligence to fill in the gaps, enhancing its efficiency. This technology is becoming increasingly accessible beyond the realm of experts, owing to the ubiquity of smartphones and the affordability of computing power. Neural Radiance Fields (NeRF) refer to neural networks capable of generating 3D images from 2D photographs. By leveraging spatial information and volumetric rendering techniques, NeRF utilizes camera positions to depict scenes in three dimensions. It can rapidly produce detailed scenes, with further training resulting in finer levels of detail being rendered..