Nvidia Corporation

Intel Corporation

University of California

Pixar

Aaron Eliot Lefohn

Marco Salvi

Anjul Patney

Christopher Ryan Wyman

David Patrick Luebke

Morgan McGuire

Carl Jacob Munkberg

Anton S Kaplanyan

Jon Niklas Theodor Hasselgren

Qi Sun

Donald Lee Brittain

Omer Shapira

Henry Packard Moreton

Eric Brian Lum

Yury Y Uralsky

Andrew T Lauritzen

Eric Enderton

Kayvon Fatahalian

Yong He

Kiril Vidimce

G Evan Hart, Jr

Yubo Zhang

Tomas G Akenine-Moller

Karthik Vaidyanathan

Timo Oskari Aila

John Matthew Burgess

Larry D Seiler

Robert M Toth

Tomas Guy Akenine-Möller

Tomasz Janczak

Jaakko Tapani Lehtinen

Peter Schuyler Shirley

Jacopo Pantaleoni

Joohwan Kim

Rasmus Barringer

Pascal Gautron

Carl Johan Gribel

Cyril Jean-Francois Crassin

Chakravarty Reddy Alla Chaitanya

Jefferson D Montgomery

John Owens

Andy Riffel

Mark Leone

Brennan Shacklett

Christoph H Schied

Tzu-Mao Li

Nir Benty

Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, at least a first optical flow network (OFN) and at least a first reconstruction network (RN) can be used to generate one or more images based, at least in part, upon the OFN and the RN using a shared loss function.

Image generation using one or more neural networks

This application discloses techniques for generating and querying projective hash maps. More specifically, projective hash maps can be used for spatial hashing of data related to N-dimensional points. Each point is projected onto a projection surface to convert the three-dimensional (3D) coordinates for the point to two-dimensional (2D) coordinates associated with the projection surface. Hash values based on the 2D coordinates are then used as an index to store data in the projective hash map. Utilizing the 2D coordinates rather than the 3D coordinates allows for more efficient searches to be performed to locate points in the 3D space. In particular, projective hash maps can be utilized by graphics applications for generating images, and the improved efficiency can, for example, enable a game streaming application on a server to render images transmitted to a user device via a network at faster frame rates.

Projective hash maps

A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Neural network system with temporal feedback for denoising of rendered sequences

Motivated by the ability of humans to quickly and accurately detect visual artifacts in images, a neural network model is trained to identify and locate visual artifacts in a sequence of rendered images without comparing the sequence of rendered images against a ground truth reference. Examples of visual artifacts include aliasing, blurriness, mosaicking, and overexposure. The neural network model provides a useful fully-automated tool for evaluating the quality of images produced by rendering systems. The neural network model may be trained to evaluate the quality of images for video processing, encoding, and/or compression techniques. In an embodiment, the sequence includes at least four images corresponding to a video or animation.

Detecting visual artifacts in image sequences using a neural network model

Neural network system with temporal feedback for adaptive sampling and denoising of rendered sequences

A global illumination data structure (e.g., a data structure created to store global illumination information for geometry within a scene to be rendered) is computed for the scene. Additionally, reservoir-based spatiotemporal importance resampling (RESTIR) is used to perform illumination gathering, utilizing the global illumination data structure. The illumination gathering includes identifying light values for points within the scene, where one or more points are selected within the scene based on the light values in order to perform ray tracing during the rendering of the scene.

Reservoir-based spatiotemporal importance resampling utilizing a global illumination data structure

A method, computer readable medium, and system are disclosed for performing spatiotemporal filtering. The method includes identifying image data to be rendered, reconstructing the image data to create reconstructed image data, utilizing a filter including a neural network having one or more skip connections and one or more recurrent layers, and returning the reconstructed image data.

Filtering image data using a neural network

A method, computer readable medium, and system are disclosed for redirecting a user's movement through a physical space while the user views a virtual environment. A temporary visual suppression event is detected when a user's eyes move relative to the user's head while viewing a virtual scene displayed on a display device, an orientation of the virtual scene relative to the user is modified to direct the user to physically move along a planned path through a virtual environment corresponding to the virtual scene, and the virtual scene is displayed on the display device according to the modified orientation.

Saccadic redirection for virtual reality locomotion

A method, computer readable medium, and system are disclosed for performing spatiotemporal filtering. The method includes the steps of applying, utilizing a processor, a temporal filter of a filtering pipeline to a current image frame, using a temporal reprojection, to obtain a color and auxiliary information for each pixel within the current image frame, providing the auxiliary information for each pixel within the current image frame to one or more subsequent filters of the filtering pipeline, and creating a reconstructed image for the current image frame, utilizing the one or more subsequent filters of the filtering pipeline.

Performing spatiotemporal filtering

A method, computer readable medium, and system are disclosed for computing a path for a user to move along within a physical space while viewing a virtual environment in a virtual reality system. A path for a user to physically move along through a virtual environment is determined based on waypoints and at least one characteristic of the physical environment within which the user is positioned, position data for the user is received indicating whether and how much a current path taken by the user has deviated from the path, and an updated path is computed through the virtual environment based on the waypoints and the at least one characteristic of the physical environment.

Path planning for virtual reality locomotion

A system, method, and computer readable medium for inverse graphics rendering comprise a differentiable rendering pipeline and a gradient descent optimization engine. A given scene is described using scene parameters. Visibility functions, and other rendered functions, are constructed to be continuous and differentiable, allowing the optimization engine and the rendering pipeline to efficiently iterate through increasingly refined scene models.

Differentiable rendering pipeline for inverse graphics

A method, computer readable medium, and system are disclosed for rendering images utilizing a foveated rendering algorithm with post-process filtering to enhance a contrast of the foveated image. The method includes the step of receiving a three-dimensional scene, rendering the 3D scene according to a foveated rendering algorithm to generate a foveated image, and filtering the foveated image using a contrast-enhancing filter to generate a filtered foveated image. The foveated rendering algorithm may incorporate aspects of coarse pixel shading, mipmapped texture maps, linear efficient anti-aliased normal maps, exponential variance shadow maps, and specular anti-aliasing techniques. The foveated rendering algorithm may also be combined with temporal anti-aliasing techniques to further reduce artifacts in the foveated image.

Perceptually-based foveated rendering using a contrast-enhancing filter

A method, computer readable medium, and system are disclosed for image processing to reduce aliasing using a temporal anti-aliasing algorithm modified to implement variance clipping. The method includes the step of generating a current frame of image data in a memory. Then, each pixel in the current frame of image data is processed by: sampling a resolved pixel color for a corresponding pixel in a previous frame of image data stored in the memory, adjusting the resolved pixel color based on a statistical distribution of color values for a plurality of samples in the neighborhood of the pixel in the current frame of image data to generate an adjusted pixel color, and blending a color value for the pixel in the current frame of image data with the adjusted pixel color to generate a resolved pixel color for the pixel in the current frame of image data.

Method for data reuse and applications to spatio-temporal supersampling and de-noising

Growing community of inventors

Kirkland, WA, United States of America

Aaron Eliot Lefohn