Abstract: A computer-implemented method comprising: receiving, from a user device, video data from a user; training a first machine learning model based on the video data to provide a second machine learning model, the second machine learning model being personalized to the user, wherein the second machine learning model is trained to predict movement of the user based on audio data; receiving further audio data from the user; determining predicted movements of the user based on the further audio data and the second machine learning model; using the predicted movements of the user to generate animation of an avatar of the user.

Abstract: Disclosed herein are various methods for supporting guidance systems. In one example, a method receives image information of a surrounding environment of the user device at a first real-world location. Thereafter, the method determines, based on the received image information, what further information is needed to generate a spatial anchor point in the model. Subsequently, the method provides guidance information to the user device for capturing the further image information that is required to create a spatial anchor point, after which point the user device may implement the guidance information. Next, the method receives further information required from the user device for generating a spatial anchor point, and then a spatial anchor point is generated based on the exchanged information.

Abstract: A tool for probing a machine learning pipeline, wherein each pipeline stage performs a respective mapping of a respective input state to a respective output state, and each but the last provides its output state on to the input state to a respective successive stage in the pipeline. At least one pipeline stage has one or more adjustable parameters which affect the respective mapping. The tool comprises: a data interface for reading probed pipeline data from the pipeline, the probed data comprising at least some of the output state of at least one pipeline stage; and a user interface module configured to present information on the probed pipeline data to a user through a user interface, and to provide at least one user interface control enabling the user to adjust one or more parameters of at least one of the stages in the pipeline based on the presented information.

Abstract: A tool for performing additional processing on a machine learning pipeline. The tool comprises: a feature extractor configured to receive intermediate pipeline data comprising at least some of the output state of one of the pipeline stages other than the last stage, and transform the intermediate pipeline data in order to derive at least one feature therefrom; and a user interface module configured to provide a user interface to a user including a control part, comprising at least a first control enabling the user to choose said at least one feature. The user interface module is configured to present the at least one feature to a user in a presentation part of the user interface.

Abstract: According to a first aspect, there is provided a computer-implemented method of controlling a user interface to selectively communicate perception results to a user, the method comprising: in response to an update instruction, using a current confidence level of each perception result of a set of perception results to determine whether or not to communicate that perception result at the user interface. The perception results are determined by processing sensor signals from a sensor system using at least one perception algorithm. At least one of the perception results is communicated at the user interface together with at least one piece of contextual information, without communicating the current confidence level that caused the perception result to be outputted, the current confidence level having been at least partially derived from the piece of contextual information.

Abstract: There is provided a computer implemented method of extracting information about a person. Incoming sensor signals for monitoring people within a field of view of a sensor system are received and processed. In response to detecting a person located within a notification region, an output device outputs a notification to the detected person. Processing of the incoming sensor signals continues in order to monitor behaviour patterns of the person and determine from his behaviour patterns whether he is currently in a consenting or non-consenting state. An extraction function attempts to extract information about the person irrespective of his determined state. A sharing function determines whether or not to share an extracted piece of information about the person with a receiving entity in accordance with his determined state, the information not being shared unless and until it is subsequently determined that the person is in the consenting state.

Abstract: A method comprising: receiving, from a user device at a first location, first image information of a surrounding environment of the user device; determining further image information of the surrounding environment of the user device at the first location, wherein the further image information is required for generating a spatial anchor in the model, wherein the spatial anchor point links the first real-world location of the user device to a corresponding location in the model; providing guidance information to the user device for capturing the further image information; receiving the further image information from the user device; and generating a spatial anchor point in the model based on the first image information and the further image information; wherein following generation of the spatial anchor point, the spatial anchor point is discoverable to one or more users of the model to provide information of the first location.