Abstract: Synthesizing high-resolution data includes distorting, with a distortion function, a region of interest (ROI) within an input of inferential data. The distorting generates distortion data within which the ROI is enhanced relative to other regions of the distortion data. A generative artificial intelligence (AI) model generates synthetic data in response to input of the distortion data. The generative AI model is trained against a distorted ground truth generated using the distortion function to distort one or more regions of interest ROI within source data used to guide the generative AI model in generating the synthetic data.

Abstract: Computer-implemented content delivery includes caching a source content having a plurality of bridge points. A user event indicating an interaction with a user is received. The user event is received during playback of the source content. In response to the user event, a template is selected from a plurality of cached templates. The template corresponds to a bridge point selected from the plurality of bridge points of the source content as an exit point from the source content. In response to the user event, a bridge is dynamically generated. The bridge links the bridge point with the template. In response to the user event, a target content is selected from a plurality of cached target contents. In response to the user event, the bridge, the template, and the target content is conveyed to a device of the user for playback following the bridge point of the source content.

Abstract: Synthesizing speech and movement of a virtual human includes capturing supplemental data generated by a transducer. The supplemental data specifies one or more attributes of a user. The capturing is performed in substantially real-time with the user providing input to a conversational platform. A behavior determiner generates behavioral data based on the supplemental data and an audio response generated by the conversational platform in response to the input to the conversation platform. Based on the behavioral data and the audio response, a rendering network generates a video rendering of a virtual human engaging in a conversation with the user, the video rendering synchronized with the audio response.

Abstract: A digital human interactive platform can determine a contextual response to a user input and generate a digital human. The digital human can convey the contextual response to the user in real time. The digital human can be configured to convey the contextual response with a predetermined behavior corresponding to the contextual response.

Abstract: A digital human development platform can enable a user to generate a digital human. The digital human development platform can receive user input specifying a dialogue for the digital human and one or more behaviors for the digital human, the one or more specified behaviors corresponding with one or more portions of the dialog on a common timeline. Scene data can be generated with the digital human development platform by merging the one or more behaviors with one or more portions of the dialogue based on times of the one or more behaviors and the one or more portions of the dialog on the common timeline.

Abstract: A digital human interactive platform can determine a contextual response to a user input and generate a digital human. The digital human can convey the contextual response to the user in real time. The digital human can be configured to convey the contextual response with a predetermined behavior corresponding to the contextual response.

Abstract: A digital human development platform can enable a user to generate a digital human. The digital human development platform can receive user input specifying a dialogue for the digital human and one or more behaviors for the digital human, the one or more specified behaviors corresponding with one or more portions of the dialog on a common timeline. Scene data can be generated with the digital human development platform by merging the one or more behaviors with one or more portions of the dialogue based on times of the one or more behaviors and the one or more portions of the dialog on the common timeline.

Abstract: In one embodiment, a computing system may access one or more frames of media content with each frame corresponding to at least part of an image. The computing system may automatically detect at least one object in at least one of the one or more frames. The computing system may automatically identify the at least one object. For each of the identified objects, the computing system may automatically determine an annotation corresponding to that object and each annotation may include one or more graphical elements. For each annotation, the computing system may automatically associate that annotation for display with its corresponding object.

Abstract: In one embodiment, a computing system may access one or more frames of media content with each frame corresponding to at least part of an image. The computing system may automatically detect at least one object in at least one of the one or more frames. The computing system may automatically identify the at least one object. For each of the identified objects, the computing system may automatically determine an annotation corresponding to that object and each annotation may include one or more graphical elements. For each annotation, the computing system may automatically associate that annotation for display with its corresponding object.