Disney Enterprises, Inc.

Evan A Binder

Anthony M Accardo

Miquel Angel Farre Guiu

Avner Swerdlow

Marc Junyent Martin

Katharine S Navarre

Matthew C Petrillo

Katharine S Ettinger

Andrew E Wood

Michael Breymann

Monica Alfaro Vendrell

Pablo Beltran Sanchidrian

Megan Stifter

Miquel Angel Farré Guiu

Jordi Badia Pujol

Jocelyn McDowell

Anna Singer

Jillian Foster

Patrick Hovsepian

Christopher Abbey

Tracia Ord

Stephanie Glover

Gino Guzzardo

Priyam S Parikh

Monica Alfaro

Drew E Perlman

Willian Padovani Germano

According to one exemplary implementation, a system includes processing hardware and a system memory storing a non-photo-realistic (NPR) renderer. The processing hardware is configured to execute the NPR renderer to receive a digital asset, display a preliminary image corresponding to the digital asset, and receive editing data identifying an NPR effect. The processing hardware is further configured to execute the software code to produce the NPR image, in a consolidated rendering and compositing process and in real-time with respect to receiving the editing data, using the preliminary image and the editing data.

Real-time non-photo-realistic rendering

A computing platform including a processing hardware and a memory storing a software code is coupled to at least first and second workstations and a production asset library. The processing hardware executes the software code to send, from the first workstation, a request identifying a production asset and metadata specification to the second workstation, and receive, from the second workstation, a payload including the production asset and production asset data including preview metadata describing a 3D image of the production asset. The software code further provides the 3D image for display on the first workstation or a third workstation, receives, at the first workstation or the third workstation, a validation input for the production asset, and publishes, in response to receiving the validation input, the production asset data to the production asset library.

Production asset library management

Certain aspects of the present disclosure provide techniques for creating interactive digital experiences for linear content. This includes identifying a plurality of assets relating to presentation of linear content. It further includes generating interactive content using the linear content, including generating an interactive sequence referencing one or more of the plurality of assets and combining the linear content with the interactive sequence on a timeline sequentially describing the linear content. The timeline includes one or more branches relating to the linear content, and selection of a first branch of the one or more branches is based on the interactive sequence. It further includes transmitting the interactive content to a user.

Creating interactive digital experiences using a realtime 3D rendering platform

Various embodiments of the invention disclosed herein provide techniques for automatically exposing 3D production assets to an editorial workstation in a content creation pipeline. A production asset management system transmits, to an editorial workstation, a first content library that includes first metadata associated with a 3D production asset that is included in an editorial cut. The first metadata allows a user to see what versions of the 3D production asset are available for incorporation into the editorial cut. The production asset management system retrieves second metadata associated with a second version of the 3D production asset from a production database. The production asset management system retrieves, based on the second metadata, the second version of the 3D production asset from the production database. The production asset management system transmits the second version of the 3D production asset to the editorial workstation for incorporation into the editorial cut.

Techniques for automatically exposing 3D production assets to an editorial workstation in a content creation pipeline

Various embodiments of the invention disclosed herein provide techniques for automatically displaying and providing electronic feedback about a 3D production asset. A client device executing a software application receives an asset data bundle associated with the 3D production asset. The client device generates a customized user interface based on at least one aspect of the asset data bundle. The client device displays the 3D production asset via the customized user interface. The client device receives an input associated with the 3D production asset via the customized user interface. The client device causes the input to be transmitted to at least one of a media content server and a production database.

Techniques for creative review of 3D content in a production environment

According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Media content metadata mapping

According to one implementation, a cloud-based system for performing cloud-based image rendering for video stream enrichment includes a video forwarding unit and a video enrichment unit. The video forwarding unit is configured to detect one or more non-interactive video player(s) linked to the video forwarding unit over a communication network, forward a video stream to the non-interactive video player(s), and forward the video stream to the video enrichment unit. The video enrichment unit is configured to receive the video stream, detect one or more interactive video player(s) linked to the video enrichment unit over the communication network, identify a video enhancement corresponding to one or more customizable video segment(s) in the video stream, insert a rendered video enhancement into the one or more customizable video segment(s) to produce an enriched video stream, and distribute the enriched video stream to one or more of the interactive video player(s).

Cloud-based image rendering for video stream enrichment

A media content tagging system includes a computing platform having a hardware processor, and a system memory storing a tag selector software code configured to receive media content having segments, each segment including multiple content elements each associated with metadata tags having respective pre-computed confidence scores. For each content element, the tag selector software code assigns each of the metadata tags to at least one tag group, determines a confidence score for each tag group based on the pre-computed confidence scores of its assigned metadata tags, discards tag groups having less than a minimum number of assigned metadata tags, and filters the reduced number of tag groups based on the second confidence score to identify a further reduced number of tag groups. The tag selector software code then selects at least one representative tag group for a segment from among the further reduced number of tag groups.

Automation of media content tag selection

Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Semiautomatic machine learning model improvement and benchmarking

This disclosure presents systems and methods to facilitate user interactions with virtual objects depicted as being present in a real-world space. A representation of a first location in a real-world space may include one or more virtual objects depicted in the representation of the first location as being present in the first location. An orientation of a computing platform physically present at a second location may be obtained. An image may be generated based on one or more of the representation of the first location, the orientation, and/or other information. The image may depict the representation of the first location within a field of view of the real-world space and one or more virtual objects depicted as being present in the first location within the field of view. The user may interact with virtual object depicted as being present in the first location within the field of view.

Growing community of inventors

Calabasas, CA, United States of America

Evan A Binder