TEMPORAL COMPOSITIONAL DENOISING

Number: 20240394851
Published: 2024-11-28
Filed: 2024-05-24
Assignee: Disney Enterprises, Inc.
Inventors: Papas; Marios et al.
CPC: G06T5/70; G06T5/60
Verdict: Low Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

Temporal compositional rendering-denoising technique.

Abstract

One embodiment of the present invention sets forth a technique for denoising video content. The technique includes converting a first frame into a first set of learned components. The technique also includes converting one or more frames that are temporally related to the first frame into one or more additional sets of learned components. The technique further includes generating, via a first machine learning model, a denoised frame corresponding to the first frame based on the first set of learned components and the one or more additional sets of learned components.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to video denoising and, more specifically, to temporal compositional denoising of video. DESCRIPTION OF THE RELATED ART

Animated movies, visual effects, video games, simulations, three-dimensional (3D) designs, and other computer graphics applications rely on rendering to simulate light transport in a 3D scene. One common rendering technique is path tracing, which involves tracing light paths from a light source as corresponding rays of light bounce around the scene before arriving at a camera. For example, a path tracing procedure may cast rays of light from the camera into the 3D scene. As a given ray of light intersects an object or medium, the ray may be absorbed, reflected, or refracted, and new rays of light may be traced from these interaction points. This process may be repeated multiple times for each pixel of a rendered image, with each path contributing a sample of light to the rendered image based on the materials and light sources encountered along the path. Path tracing uses Monte Carlo techniques to randomly sample these light paths, which allows for the simulation of complex optical effects such as soft shadows, depth of field, motion blur, indirect lighting, caustics, and global illumination.

However, because path tracing relies on random sampling, the number of samples required to accurately capture light transport in a scene is typically p

Claims

1. A computer-implemented method for denoising video content, the method comprising: converting a first frame into a first set of learned components; converting one or more frames that are temporally related to the first frame into one or more additional sets of learned components; and generating, via a first machine learning model, a denoised frame corresponding to the first frame based on the first set of learned components and the one or more additional sets of learned components. || 11. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: converting a first frame into a first set of learned components; converting one or more frames that are temporally related to the first frame into one or more additional sets of learned components; and generating, via a first machine learning model, a denoised frame corresponding to the first frame based on the first set of learned components and the one or more additional sets of learned components. || 20. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: converting a first frame into a first set of learned components; converting one or more frames that are temporally related to the first frame into one or more additional sets of learned components; and generating, via a first machine learning model, a denoised frame corresponding to the first frame based on the first set of learned components and the one or more additional sets of learned components.