Disney Enterprises, Inc.

Eth Zurich

Eth Zurich (eidgenossische Technische Hochschule Zurich)

Hayko Jochen Wilhelm Riemenschneider

Christopher Richard Schroers

Abdelaziz Djelouah

Leonhard Markus Helminger

Erika Varis Doggett

Evan Goldberg

Shinobu Hattori

Techniques for automatically placing and manipulating virtual objects of augmented reality (AR) and mixed reality (MR) simulations are described. One technique includes obtaining an indication of at least one virtual object available for placing within a real-world environment during an AR simulation or a MR simulation. A first representation of the real-world environment is generated, based on a scan of the real-world environment. At least one second representation of the real-world environment is generated from the first representation. A match is determined between the at least one virtual object and at least one available space within the real-world environment, based at least in part on evaluating the at least one virtual object and the at least one second representation with a machine learning model(s). The at least one virtual object is rendered on a computing device, based on the match.

Space and content matching for augmented and mixed reality

Generating augmented reality content includes inputting a first layout of a physical space and a first set of anchor content into a machine learning model; generating, via execution of the machine learning model, a first augmented reality view that includes (i) a first portion of the physical space and (ii) an extension of the first set of anchor content across a second portion of the physical space; and causing the first augmented reality view to be outputted in a computing device.

Neural extension of 3D content in augmented reality environments

A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Data object classification using an optimized neural network

A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.

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Zurich, Switzerland

Hayko Jochen Wilhelm Riemenschneider