A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

A computer-implemented method is disclosed. The method includes: receiving video data for a live video stream; identifying a product that is displayed in a video frame of the live video stream; obtaining identifying information for a viewer of the live video stream; determining a product variant preference for the viewer; obtaining a graphical representation of the product variant; and providing modified video data of the live video stream based on presenting the graphical representation of the product variant as overlay content for the live video stream. Related apparatuses and computer-readable media are also disclosed.

The disclosure relates to a method and an apparatus for dynamic scene updates (scenes including timed media) requiring extensions to scene description patch documents supporting timed media. A method for timed and event triggered updates in a scene description for extended reality (XR) multimedia is provided. The method includes parsing a scene description and at least one scene update track sample in a timed scene description update track or at least one scene update item to update the scene description and rendering the updated scene description on a display.

An example method performed by a processing system includes retrieving a digital model of a media element from a database storing a plurality of media elements, wherein the media element is to be inserted into a scene of an audiovisual media, rendering the media element in the scene of the audiovisual media, based on the digital model of the media element and on metadata associated with the digital model to produce a rendered media element, wherein the metadata describes a characteristic of the media element and a limit on the characteristic, and inserting the rendered media element into the scene of the audiovisual media.

Systems, apparatuses and methods may provide for technology that sends a first message via an input output (IO) link, wherein the first message includes a first rendering asset and an identifier (ID) associated with the first rendering asset. The technology may also exclude a second rendering asset from a second message in response to the ID being shared by the first rendering asset and the second rendering asset and send the second message via the IO link, wherein the second message includes the ID. In one example, the ID is a hash ID.

Content controller system comprising rendering server system comprising a plurality of servers. The servers receiving a plurality of segment render requests that correspond respectively to segments included in a set of media content item segments. The servers render the segments corresponding to the segment render requests using a media content identification and a main user identification. Rendering the segments comprises retrieving metadata from a metadata database associated with the media content identification, rendering the segments using the metadata, generating a main user avatar based on the main user identification, and incorporating the main user avatar into the segments. The servers can upload the segments to a segment database and update segment states in a segment state database to indicate that the segments are available. Other embodiments are disclosed herein.

Analyzing the complexity of an object of a scene in a media steam (or media data) performed by at least one processor, is provided, including receiving immersive media data comprising a plurality of scenes from a content source; obtaining a respective object of a respective scene in the plurality of scenes, from the immersive media data; analyzing the respective scene to generate complexity information associated with the respective object of the respective scene; generating metadata associated with the respective object of the respective scene, the metadata comprising the complexity information; and determining whether to distribute the respective scene to a client for processing based on the generated metadata.

The invention relates to an integrated circuit arrangement, in particular an FPGA or an ASIC, for processing video data. It has a number of video interfaces for receiving and/or emitting video data from a plurality of data streams, as well as a frame buffer control system and a frame buffer interface. The invention further relates to a network device, a network, a computer unit and a method for processing video data.

A computing infrastructure for movie making, product placements in the movie, etc. It provides movie creation and post-production services and supports clearance. Product placement services are provided for digital media of all kinds such as movies, video clips, audio clips, etc. The processing starts when a new digital media is created/uploaded by a user. Then detection of placeholder objects (one or more) is facilitated. The output is a catalog of placeholder objects. The user can review this output and modify it, and store it for subsequent processing. Then the user can replace placeholder objects with product placement material to create a modified digital media that is distributed.

A cloud computing system can be configured to generate data models. A model optimizer of the cloud computing system can provision computing resources of the cloud computing system with a data model. A dataset generator of the cloud computing system can generate a synthetic dataset for training the data model. The computing resources can train the data model using the synthetic dataset. The model optimizer can store the data model and metadata of the data model in a model storage. The cloud computing system can receive production data from a data source by a production instance of the cloud computing system using a common file system. The production data can be processed using the data model by the production instance. The computing resources, the dataset generator, and the model optimizer can be hosted by separate virtual computing instances of the cloud computing system.