Neural Rendering: A Brief Overview

Neural rendering uses deep neural networks to create new images and video from existing scenes. The camera angles, lighting, and other details can be rendered into a realistic model of a 3D scene. In addition, neural rendering of existing images and videos can be used to generate synthetic data.

Why it matters: Traditional 3D graphic rendering needs a model with a polygon mesh describing shape, color, and textures, as well as the lighting and camera position. Neural rendering simulates camera physics to separate the 3D scene from the camera capture process, making it easier to create new images from existing images and videos with consistency.

Applications:

Semantic Photo Manipulation and Synthesis — Allows interactive editing to modify images based on semantic meaning.

Novel View Synthesis — Generates new camera perspectives with a limited dataset.

Volumetric Performance Capture — Uses multiple cameras to learn 3D geometries and textures; lacks photorealism and is challenging to make temporally consistent.

Relighting — Can create relisting rendering scenes with different lighting conditions. It is essential for augmented reality (AR).

Dive deeper: Check out Neural Rendering: A Gentle Introduction by Datagen for an in-depth overview of the technology behind neural rendering and a deeper look at the applications.