Nvidia Corporation

Rev Lebaredian

John F Spitzer

Dmitry Duka

Tony Tamasi

Michael Kass

Brian Harris

Andrey Shulzhenko

Yury Y Uralsky

Gary D Hicok

Michael Brian Cox

John R Zedlewski

Greg Heinrich

Jonathan Small

Clement Farabet

Claire Delaunay

David Auld

Vasiliy Skvortsov

Zachary Taylor

Ivan Shutov

Mark Daly

Matthew Campbell

Mark J Kilgard

Cass Watson Everitt

William R Mark

Ratin Kumar

Jen-Hsun Huang

Jonathan Cohen

Santanu Dutta

Matthew Milton Pharr

Craig E Kolb

Christopher T Dodd

Simon Yuen

Curtis A Beeson

Curtis Beeson

Chad Vivoli

Vasiliv Skvortsov

A cloud-centric platform is used for generating virtual three-dimensional (3D) content, that allows users to collaborate online and that can be connected to different software tools (applications). Using the platform, virtual environments (e.g., scenes, worlds, universes) can be created, accessed, and interacted with simultaneously by multiple collaborative content creators using varying content creation or development applications.

Platform and method for collaborative generation of content

In various examples, a virtually animated and interactive agent may be rendered for visual and audible communication with one or more users with an application. For example, a conversational artificial intelligence (AI) assistant may be rendered and displayed for visual communication in addition to audible communication with end-users. As such, the AI assistant may leverage the visual domain—in addition to the audible domain—to more clearly communicate with users, including interacting with a virtual environment in which the AI assistant is rendered. Similarly, the AI assistant may leverage audio, video, and/or text inputs from a user to determine a request, mood, gesture, and/or posture of a user for more accurately responding to and interacting with the user.

Conversational AI platform with rendered graphical output

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

Training, testing, and verifying autonomous machines using simulated environments

A content management system may maintain a scene description that represents a 3D virtual environment and a publish/subscribe model in which clients subscribe to content items that correspond to respective portions of the shared scene description. When changes are made to content, the changes may be served to subscribing clients. Rather than transferring entire descriptions of assets to propagate changes, differences between versions of content may be exchanged, which may be used construct updated versions of the content. Portions of scene description may reference other content items and clients may determine whether to request and load these content items for lazy loading. Content items may be identified by Uniform Resource Identifiers (URIs) used to reference the content items. The content management system may maintain states for client connections including for authentication, for the set of subscriptions in the publish/subscribe model, and for their corresponding version identifiers.

Cloud-centric platform for collaboration and connectivity on 3D virtual environments

Embodiments of the present invention provide a novel solution that uses subjective end-user input to generate optimal image quality settings for an application. Embodiments of the present invention enable end-users to rank and/or select various adjustable application parameter settings in a manner that allows them to specify which application parameters and/or settings are most desirable to them for a given application. Based on the feedback received from end-users, embodiments of the present invention may generate optimal settings for whatever performance level the end-user desires. Furthermore, embodiments of the present invention may generate optimal settings that may be benchmarked either on a server farm or on an end-user's client device.

Method and system for customizing optimal settings using end-user preferences

Multiple snapshots of a scene are captured within an executing application (e.g., a video game). When each snapshot is captured, associated color values per pixel and a distance or depth value z per pixel are stored. The depth information from the snapshots is accessed, and a point cloud representing the depth information is constructed. A mesh structure is constructed from the point cloud. The light field(s) on the surface(s) of the mesh structure are calculated. A surface light field is represented as a texture. A renderer uses the surface light field with geometry information to reproduce the scene captured in the snapshots. The reproduced scene can be manipulated and viewed from different perspectives.

Virtual photogrammetry

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment&#x2014;in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack&#x2014;to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

A system, method, and computer program product are provided for simultaneously determining settings for a plurality of parameter variations. In use, a plurality of parameter variations associated with a device is identified. Additionally, settings for each of the plurality of parameter variations are determined simultaneously.

System, method, and computer program product for simultaneously determining settings for a plurality of parameter variations

The disclosure provides a virtual view broadcaster, a virtual view broadcasting system, and a video gaming broadcaster. In one embodiment, the virtual view broadcaster includes: (1) a cloud-based renderer configured to generate virtual view images from a virtual camera positioned in a computer application, and (2) an image processor configured to generate a virtual view stream for the virtual camera employing the virtual view rendered images, wherein the virtual view images are from different viewing directions at the virtual camera.

Cloud generation of content to be streamed to VR/AR platforms using a virtual view broadcaster

A system, method, and computer program product are provided for determining parameters for an application based on hardware specifications. In operation, a plurality of hardware specifications associated with a system are identified. Additionally, at least one application associated with the system is identified. Furthermore, at least one parameter for the application is determined based on the hardware specifications. Still yet, the parameter is applied to the application.

System, method, and computer program product for determining application parameters based on hardware specifications

A system, method, and computer program product are provided for determining a plurality of application settings utilizing a mathematical function. In operation, a plurality of application parameters are identified. Additionally, the application parameters are defined as a mathematical function. Furthermore, a plurality of application settings are determined utilizing the mathematical function.

System, method, and computer program product for determining a plurality of application settings utilizing a mathematical function

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Austin, TX, United States of America

Rev Lebaredian