Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method includes obtaining source video data comprising a plurality of image frames, and using a face tracker to detect one or more instances of faces within respective sequences of image frames of the source video data. For a first instance of a given face detected within a first sequence of image frames, the method includes determining a framewise location and size of the first instance of the given face in the first sequence of image frames, using a neural renderer to obtain replacement video data comprising a replacement instance of the given face, and using the determined framewise location and size to replace at least part of the first instance of the given face with at least part of the replacement instance of the given face.

Abstract: A computer implemented method includes receiving, from a remote system, object data comprising values of a set of adjustable parameters of a respective object representation model for each object of a plurality of objects and compositing data indicating at least a respective trajectory of each of the plurality of objects relative to a reference frame. The method includes processing the object data and the compositing data using the respective object representation models to generate output video data depicting a scene including an animated representation of each object of the plurality of objects following the respective trajectory relative to the reference frame. For each object of the plurality of objects, the respective object representation model comprises a respective neural network and is arranged to generate, using the respective neural network, animated representations of the object in which a geometry the object is controllable by the set of adjustable parameters.

Abstract: Obtaining input video data depicting footage of an object, obtaining current values of a set of adjustable parameters of an object representation model comprising a neural network and arranged to generate animated representations of the object using the neural network in which a geometry of the object is controllable by the set of adjustable parameters. For a plurality of iterations, using the object representation model to generate a video layer comprising an animated representation of the object in which the geometry of the object corresponds to the current values of the set of adjustable parameters, presenting composite video data comprising the video layer overlaid on the object in the input video data, and updating the current values of the set of adjustable parameters in response to user input via the user interface.

Abstract: Obtaining input video data depicting footage of an object, obtaining current values of a set of adjustable parameters of an object representation model comprising a neural network and arranged to generate animated representations of the object using the neural network in which a geometry of the object is controllable by the set of adjustable parameters. For a plurality of iterations, using the object representation model to generate a video layer comprising an animated representation of the object in which the geometry of the object corresponds to the current values of the set of adjustable parameters, presenting composite video data comprising the video layer overlaid on the object in the input video data, and updating the current values of the set of adjustable parameters in response to user input via the user interface.

Abstract: A computer implemented method includes receiving, from a remote system, object data comprising values of a set of adjustable parameters of a respective object representation model for each object of a plurality of objects and compositing data indicating at least a respective trajectory of each of the plurality of objects relative to a reference frame. The method includes processing the object data and the compositing data using the respective object representation models to generate output video data depicting a scene including an animated representation of each object of the plurality of objects following the respective trajectory relative to the reference frame. For each object of the plurality of objects, the respective object representation model comprises a respective neural network and is arranged to generate, using the respective neural network, animated representations of the object in which a geometry the object is controllable by the set of adjustable parameters.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: Obtaining input video data depicting footage of an object, obtaining current values of a set of adjustable parameters of an object representation model comprising a neural network and arranged to generate animated representations of the object using the neural network in which a geometry of the object is controllable by the set of adjustable parameters. For a plurality of iterations, using the object representation model to generate a video layer comprising an animated representation of the object in which the geometry of the object corresponds to the current values of the set of adjustable parameters, presenting composite video data comprising the video layer overlaid on the object in the input video data, and updating the current values of the set of adjustable parameters in response to user input via the user interface.

Abstract: A computer-implemented method of processing video data comprising a sequence of image frames. The method includes isolating an instance of an object within the sequence of image frames, generating a modified instance of the object using a machine learning model, and modifying the video data to smoothly transition between at least part of the isolated instance of the object and a corresponding at least part of the modified instance of the object over a subsequence of the sequence of image frames.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method includes obtaining source video data comprising a plurality of image frames, and using a face tracker to detect one or more instances of faces within respective sequences of image frames of the source video data. For a first instance of a given face detected within a first sequence of image frames, the method includes determining a framewise location and size of the first instance of the given face in the first sequence of image frames, using a neural renderer to obtain replacement video data comprising a replacement instance of the given face, and using the determined framewise location and size to replace at least part of the first instance of the given face with at least part of the replacement instance of the given face.

Abstract: A computer-implemented method of processing video data comprising a sequence of image frames. The method includes isolating an instance of an object within the sequence of image frames, generating a modified instance of the object using a machine learning model, and modifying the video data to smoothly transition between at least part of the isolated instance of the object and a corresponding at least part of the modified instance of the object over a subsequence of the sequence of image frames.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A computer-implemented method of processing video data comprising a first sequences of image frames containing a first instance of an object. The method includes isolating said first instance of the object within the first sequence of image frames, determining, using the isolated first instance of the object, first parameter values for a synthetic model of the object, modifying the first parameter values for the synthetic model of the object, rendering a modified first instance of the object using a trained machine learning model and the modified first parameter values for the synthetic model of the object, and replacing at least part of the first instance of the object within the first sequence of image frames with a corresponding at least part of the modified first instance of the object.

Abstract: A control device for the selective control of a multiplicity of actuators of an internal combustion engine is provided. The control device includes a central control unit, and a multiplicity of actuators associated with the multiplicity of actuator controls, which are connected to the central control unit. Each actuator control, of the multiplicity of actuator controls, includes an address, so that the actuator controls are selectively controllable by the central control unit by means of the address. Also, each actuator control includes a comparator, which is configured to compare a signal, preferably a voltage or current signal, emitted by the central control unit with the address. The comparator also emits an activation signal via an activation line, if the comparison has resulted in a match of the signal with the address of the respective actuator control within a window, preferably a voltage window (?V).

Abstract: A control device for the selective control of a multiplicity of actuators of an internal combustion engine is provided. The control device includes a central control unit, and a multiplicity of actuators associated with the multiplicity of actuator controls, which are connected to the central control unit. Each actuator control, of the multiplicity of actuator controls, includes an address, so that the actuator controls are selectively controllable by the central control unit by means of the address. Also, each actuator control includes a comparator, which is configured to compare a signal, preferably a voltage or current signal, emitted by the central control unit with the address. The comparator also emits an activation signal via an activation line, if the comparison has resulted in a match of the signal with the address of the respective actuator control within a window, preferably a voltage window (?V).

Abstract: The present invention is a method for losing weight and optimizing body composition comprising the steps of eating five meals a day with 1 serving of vegetables or fruit, complex carbohydrate, and lean protein for approximately 300 calories per meal at ranges of 25-35% protein, 50-55% carbohydrate, 15-20% fat with six to seven grams of fiber for a total of 1,500 calories per day for women. The method also comprises a method for losing weight and optimizing body composition for men comprising the steps of eating five meals a day every 2½ to 3½ hours consisting of 1½ servings of vegetables or fruit, complex carbohydrate, and lean protein for 450 calories per meal at 25-35% protein, 50-55% carbohydrate, and 15-20% fat with nine to ten grams of fiber for a total of 2,250 calories per day.