In some examples, a computing device may receive a request for a video segment captured by a particular camera, where the request specifies a date, a start time, and a length of the video segment. The computing device may identify stored data associated with the video segment in a storage device based on the date, the start time, the length, and an identifier associated with the particular camera and retrieve the stored data from the storage device. The computing device may determine that the stored data includes a subset of the video frames that were sent from the particular camera and excludes a remainder of the video frames and regenerate the remainder of the video frames based on the stored data to create regenerated data. The computing device may reconstruct the reconstructed video segment by merging the stored data with the regenerated data and provide the reconstructed video segment.

In some examples, a computing device may receive a request for a video segment captured by a particular camera, where the request specifies a date, a start time, and a length of the video segment. The computing device may identify stored data associated with the video segment in a storage device based on the date, the start time, the length, and an identifier associated with the particular camera and retrieve the stored data from the storage device. The computing device may determine that the stored data includes a subset of the video frames that were sent from the particular camera and excludes a remainder of the video frames and regenerate the remainder of the video frames based on the stored data to create regenerated data. The computing device may reconstruct the reconstructed video segment by merging the stored data with the regenerated data and provide the reconstructed video segment.

A search engine is modified to perform increasingly precise image searching using iterative Natural Language (NL) interactions. From an NL search input, the modification extracts a set of input features, which includes a set of response features corresponding to an NL statement in the NL search input and a set of image features from a seed image in the NL search input. The modification performs image analysis on an image result in a result set of a query including at least some of the input features. In a next iteration of NL interactions, at least some of the result set is provided. An NL response in the iteration is added to a cumulative NL basis, and a revised result set is provided, which includes a new image result corresponding to a new response feature extracted from the cumulative NL basis.

Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

Techniques for generating an image are disclosed. In some embodiments, a received input image is transformed to generate an output image using a machine learning based framework that is trained on a constrained set of images. The generated output image comprises an attribute learned by the machine learning based framework from the set of images.

A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

The techniques disclosed herein improve the efficiency of a system by providing intelligent agents for managing data associated with objects that are displayed within mixed-reality and virtual-reality collaboration environments. Individual agents are configured to collect, analyze, and store data associated with individual objects in a shared view. The agents can identify real-world objects and virtual objects discussed in a meeting, collect information about each object and generate recommendations for each object based on the collected information. The recommendations can suggest modifications to the objects, provide resources for obtaining or modifying the objects, and provide actionable information allowing users to reach a consensus regarding an object. The data can be shared between different communication sessions without requiring users to manually store and present a collection of content for each object. The intelligent agents can also persist through different communication sessions to enhance user engagement and improve productivity.

There is provided a system and method of collecting geospatial object data with mediated reality. The method including: receiving a determined physical position; receiving a live view of a physical scene; receiving a geospatial object to be collected; presenting a visual representation of the geospatial object to a user with the physical scene; receiving a placement of the visual representation relative to the physical scene; and recording the position of the visual representation anchored into a physical position in the physical scene using the determined physical position.

Determining contextual confidence of images for associative deep learning includes receiving an image including a representation of a subject. Text data related to the image is received. One or more physical properties of the image are determined. Context information of the image is determined using natural language processing. The image is classified based upon the contextual information and the one or more physical properties using a classification model to determine a classification. An emotional state of the image is determined based upon the physical properties. A confidence of the classification and emotional state is determined.