Pixar

Disney Enterprises, Inc.

Eyecam LLC

Flow-tech Systems, LLC

Other

Package, Inc.

Ford Global Technolgoies, LLC

Eth Zurich

Eth Zurich (eidgenossische Technische Hochschule Zurich)

Foundation Productions, LLC

Msl Oilfield Services Limited

Disnev Enterprises, Inc.

Clearwell Oilfield Solutions Limited

Mark Joseph Meyer

Jan Novak

Brian McWilliams

Thijs Vogels

Fabrice Rousselle

Bryan Jonathan Davis

Ronald Eugene Fisher

James Mark Fisher

Bradley Brian Bushard

Nitin Patil

John R Anderson

Tony DeRose

Alex Harvill

George Rihovsky

Christopher Richard Schroers

Anthony David DeRose

Ben Young

Daniel Johnson

Abdelaziz Djelouah

Fernando Ferrari De Goes

Steve Bako

Forrester Cole

Sanjay Bakshi

David Fraser

Andrew David Cater

Karlis Martins Briedis

Ian Mcgonigal

Markus H Gross

Robert Walker Sumner

Basavaraj Tonshal

Stelian Coros

John William Schmotzer

Bernhard Thomaszewski

Lena Petrovic

Marios Papas

Rasmus Tamstorf

Panduranga Kondoju

Harminder Singh Sandhu

Daniel Teece

Charles Tappan

Martin Clark

Brian Green

Fabian Hahn

Tom Sanocki

Jamal Alezzani

Hayley Iben

Olivier Soares

Sarah Radzihovsky

Anthony Derose

Vaibhav Vavilala

Joan Massich

Antoine Milliez

Hayko Riemenschneider

Clara Fernandez

Markus Plack

Raphael François Otriz

Mohammad Rahman

John Anderson

Embodiments of the present invention are directed to methods and systems for treating irrigation water by introducing a propagating electromagnetic field into the irrigation water as it flows through an irrigation system. The treatments described herein may have a variety of beneficial effects when applied to hydrophobic soils, including a significant increase in the percentage of the water that is maintained in the root zone of a given crop as plant-available water and the essential mineral, e.g. calcium and/or magnesium, uptake of that crop. The treatments described herein may also have beneficial effects when applied to hydrophilic soils, and particularly high salinity, hydrophilic soils, including a reduction in the soil salinity, including the amount of chlorates, within a root zone of a crop.

Method and system for treating irrigation water

A system includes a processor and a memory storing software code. The processor executes the software code to receive a first rendered image having first color and auxiliary buffers, receive a second rendered image having second color and auxiliary buffers, provide a third auxiliary buffer for an intermediate image to be interpolated, and identify one or more context features from each of the color buffers and the auxiliary buffers. The software code further interpolates, based on the context features, the intermediate image to provide an interpolated image, determines, based on the one or more context features from each of the first, second, and third auxiliary buffers, a rendering mask for producing one or more rendered sub-portions of the intermediate image, partially renders, using the rendering mask, the intermediate image to provide a partially rendered image, and blends the interpolated image and the partially rendered image to provide a composite image.

Kernel-based frame interpolation for spatio-temporally adaptive rendering

One embodiment of the present invention sets forth a technique for performing frame interpolation. The technique includes generating (i) a first set of feature maps based on a first set of rendering features associated with a first key frame, (ii) a second set of feature maps based on a second set of rendering features associated with a second key frame, and (iii) a third set of feature maps based on a third set of rendering features associated with a target frame. The technique also includes applying one or more neural networks to the first, second, and third set of feature maps to generate a set of mappings from a first set of pixels in the first key frame to a second set of pixels in the target frame. The technique further includes generating the target frame based on the set of mappings.

Frame interpolation for rendered content

Techniques for aligning object representations for animation include analyzing a source object representation and a target object representation to identify a category of the source object and a category of the target object. Based on the category or categories of the objects, a feature extraction machine learning models is selected. The source object representation and the target object representation are provided as input to the selected feature extraction machine learning model to generate respective semantic descriptors and shape vectors for the source and target objects. Based on the semantic descriptors and the shape vectors for the source and target objects, an alignment machine learning model generates an aligned target object representation that is aligned with the source object representation and usable for animating the target object.

Object alignment techniques for animation

A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.

Temporal techniques of denoising Monte Carlo renderings using neural networks

A shipping package () comprises an enclosure for receiving content within, a closure () for sealing the enclosure, a label comprising shipping information, a network module (), a sensor module (), and a battery module (). The battery module provides power to the network module and the sensor module. The sensor module () providing location information to the network module, the network module transmits a shipping status message to an external device.

Shipping package tracking or monitoring system and method

A method performed by a computer is disclosed. The method comprises receiving color data for input pixels of an input image and an input set of features used to render the input image of a three-dimensional animation environment, wherein the input pixels are of a first resolution. The computer may then load into memory a generator of a generative adversarial network including a neural network used to scale the input image, the neural network trained using training data comprising color data of training input images and training output images and a training set of the features used to render the training input images. After the generator is loaded into memory, the computer may generate an output image having a second resolution that is different than the first resolution by passing the color data and the input set of features through the generator.

Deep learned super resolution for feature film production

Systems and methods generate a modified three-dimensional mesh representation of an object using a trained neural network. A computer system receives a set of input values for posing an initial mesh defining a surface of a three-dimensional object. The computer system provides the input values to a neural network trained on posed meshes generated using a rigging model to generate mesh offset values based upon the set of input values and the initial mesh. The neural network includes an input layer, an output layer, and a plurality of intermediate layers. The computer system generates, by the output layer of the neural network, a set of offset values corresponding to a set of three-dimensional target points based on the set of input values. The offset values are applied to the initial mesh to generate a posed mesh. The computer system outputs the posed mesh for generating an animation frame.

Object deformation network system and method

Embodiments of the present invention are directed to methods and systems for treating irrigation water by introducing a propagating electromagnetic field into the irrigation water as it flows through an irrigation system. The treatments described herein may have a variety of beneficial effects on the water, including a significant increase in the percentage of the water that is maintained in the root zone of a given crop as plant-available water and the essential mineral, e.g. calcium and/or magnesium, uptake of that crop.

A gateway provides duplex-directional, multimedia ad hoc mesh networking, peer-to-peer direct communications, power optimization, dynamic configuration, and data management, while operating within various devices and network topologies. A multitasking virtual machine monitor for mobile networked devices, that is capable of functional expandability and portability to various operating environments, interoperability with a variety of operating systems, the Gateway Control System (GCS) performs functional capabilities in both a local and a networked topology using local and remote hardware and software. Software within the GCS is partitioned into sequentially, autonomous code, referred to herein as 'modules,' each module being configured to communicate with hardware and other gateway modules. Collectively, all gateway modules are referred to herein as the gateway stack (GS). Each member of the GS can be turned on or off, downloaded from a remote site, and dynamically configured.

Modular device and data management system and gateway for a communications network

Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Denoising Monte Carlo renderings using progressive neural networks

The present disclosure relates to using a neural network to efficiently denoise images that were generated by a ray tracer. The neural network can be trained using noisy images generated with noisy samples and corresponding denoised or high-sampled images (e.g., many random samples). An input feature to the neural network can include color from pixels of an image. Other input features to the neural network, which would not be known in normal image processing, can include shading normal, depth, albedo, and other characteristics available from a computer-generated scene. After the neural network is trained, a noisy image that the neural network has not seen before can have noise removed without needing manual intervention.

De-noising images using machine learning

Growing community of inventors

San Francisco, CA, United States of America

Mark Joseph Meyer