Abstract: A method and system for transforming an input image via a plurality of image transformation stylizers includes receiving the input image; providing the input image, information about the plurality of image transformation stylizers and at least one of user data, history data, and contextual data to a trained machine-learning (ML) model for selecting a subset of the plurality of image transformation stylizers; receiving as an output from the ML model the subset of image transformation stylizers; executing the subset of the image transformation stylizers on the input image to generate a plurality of transformed output images; ranking the plurality of transformed output images based on at least one of the input image, the user data, the history data, and the contextual data; and providing the ranked plurality of transformed output images for display.

Abstract: A method and system for transforming an input image via a plurality of image transformation stylizers includes receiving the input image; providing the input image, information about the plurality of image transformation stylizers and at least one of user data, history data, and contextual data to a trained machine-learning (ML) model for selecting a subset of the plurality of image transformation stylizers; receiving as an output from the ML model the subset of image transformation stylizers; executing the subset of the image transformation stylizers on the input image to generate a plurality of transformed output images; ranking the plurality of transformed output images based on at least one of the input image, the user data, the history data, and the contextual data; and providing the ranked plurality of transformed output images for display.

Abstract: The present disclosure relates to application of artificial intelligence (AI) processing that adapts one or more video feeds relative to presentation content. Trained AI processing automatically generates a combined representation comprising one or more video feeds and presentation content. An exemplary combined representation is the result of contextual analysis by one or more trained AI models that are adapted to consider how to adapt presentation of a video feed relative to displayable presentation content (or visa-versa). A combined representation of one or more video feeds and presentation content is automatically generated (and subsequently rendered) based on a result of contextual evaluation of data associated with a video feed and data attributes of presentation content. A combined representation may comprise a modification of the one or more video feeds, objects of presentation content or a combination thereof.

Abstract: A method and system for generating training data for training a gesture detection machine-learning (ML) model includes receiving a request to generate training data for the gesture detection model, the training data being associated with a target gesture, retrieving data associated with an original gesture, the original gesture being a gesture made using a body part, retrieving skeleton data associated with the target gesture, the skeleton data displaying a skeleton representative of the body part and the skeleton displaying the target gesture, aligning a location of the body part in the data with a location of the skeleton in the skeleton data, providing the aligned data and the skeleton data to an ML model for generating a target data that displays the target gesture, receiving the target data as an output from the ML model, the target data preserving a visual feature of the data and displaying the target gesture, and providing the target data to the gesture detection ML model.

Abstract: Aspects of the present disclosure relate to systems and methods for detecting and correcting undesirable content. A video feed may be segmented to distinguish background data from foreground data. It may be determined that a region of the background data includes a qualifying behavior. The qualifying behavior may be classified as belonging to a distracting category of data. An effect may be applied to the background data that includes the qualifying behavior to reduce an appearance of the qualifying behavior.