Neural Radiance Fields (NeRF)

Given a number of images of the same scene, a neural network can synthesize images from novel vantage points, but it can take hours to train. A new approach cuts training time to a few minutes.

To reconstruct the 3D world behind a set of 2D images, machine learning systems usually require a dedicated neural network for each scene. New research enables a single trained network to generate 3D reconstructions of multiple scenes.

Researchers have used neural networks to generate novel views of a 3D scene based on existing pictures plus the positions and angles of the cameras that took them. In practice, though, you may not know the precise camera

Ideally, real-time 3D applications such as virtual and augmented reality transition smoothly between different viewpoints of a scene — but generating a fresh perspective can take time. New research speeds the process.

The University of Hong Kong, and Cornell University to create Neural Body, a procedure that generates novel views of a single human character based on shots from only a few angles.