Nvidia Corporation

Arun Mohanray Mallya

Ming-Yu Liu

Ting-Chun Wang

Samuli Matias Laine

David Patrick Luebke

Timo Oskari Aila

Tero Tapani Karras

Jaakko Tapani Lehtinen

Miika Samuli Aittala

Karan Sapra

Jan Kautz

Arash Vahdat

Abhijit Patait

Aurobinda Maharana

Zekun Hao

Siddharth Gururani

Jose Rafael Valle Da Costa

Apparatuses, systems, and techniques are presented to synthesize consistent images or video. In at least one embodiment, one or more neural networks are used to generate one or more second images based, at least in part, on one or more point cloud representations of one or more first images.

Video synthesis using one or more neural networks

Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, upon one or more semantic features projected from a three-dimensional environment.

Generating images of virtual environments using one or more neural networks

Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate video information based at least in part upon voice information and a combination of image features and facial landmarks corresponding to one or more images of a person.

Speech-driven animation using one or more neural networks

In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.

Machine-learning-based architecture search method for a neural network

Systems and methods herein address reference frame selection in video streaming applications using one or more processing units to decode a frame of an encoded video stream that uses an inter-frame depicting an object and an intra-frame depicting the object, the intra-frame being included in a set of intra-frames based at least in part on at least one attribute of the object as depicted in the intra-frame being different from the at least one attribute of the object as depicted in other intra-frames of the set of intra-frames.

Frame selection for streaming applications

A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

Generative adversarial neural network assisted video reconstruction

Generative adversarial neural network assisted compression and broadcast

Generative adversarial neural network assisted reconstruction

Generative adversarial neural network assisted video compression and broadcast

Growing community of inventors

San Jose, CA, United States of America

Arun Mohanray Mallya