Sony Interactive Entertainment Inc.

Timothy Edward Bradley

Fabio Cappello

Matthew William Sanders

Oliver George Hume

Maria Chiara Monti

Andrew James Bigos

Marina Villanueva Barreiro

Jason Craig Millson

Patrick John Connor

Andrew William Walker

Nigel John Williams

David Erwan Damien Uberti

Maurizio Cerrato

Jessica Rose Grant

Gregory James Bedwell

Rajeev Gupta

Gilles Christian Rainer

Daryl Cooper

Guy Moss

An image processing system for modifying one or more images of a user to change a cardiovascular activity that is measurable from the one or more images, the system comprising: an image receiving section configured to receive image data comprising a plurality of image frames; a region selection section configured to select one or more regions where the colour of the skin of the user changes temporally in the image data, wherein the colour changes are indicative of the cardiovascular activity of the user; and a modifying section configured to modify the colour change of the one or more selected regions in one or more image frames to create modified image data, as described elsewhere herein.

Image processing system and method

A method of generating video frames for a cloud-based video game includes obtaining, via a communications network, a plurality of frames of a video game rendered at a cloud device; obtaining a player input received at an input device, the input device being used to play the video game, the player input being received in response to the display of at least one of the obtained video frames; inputting at least one of the obtained video frames and the player input to a prediction model; and synthesizing, based on the output of the prediction model, a subsequent frame for output at a display, and outputting the synthesized video frame at the display.

Method and device for generating video frames

A method of controlling at least a first non-player object within a virtual environment of an application includes: selecting a template non-player object behavioural AI model, the AI model being previously trained separately to the application using reinforcement learning to characterise behaviour of a respective non-player object type, from among a plurality of template AI models, associating one or more non-player objects of the application with the selected template AI model, and for the or each associated non-player object, inputting application state information to an input interface of the template AI model for receiving state information relevant to the associated non-player object's type, and receiving from an output interface of the template AI model non-player object state information, and then updating the state of the respective non-player object for the virtual environment of the application responsive to the received non-player object state information.

Virtual environment development method and system

An image rendering method for rendering a pixel at a viewpoint includes, for a first element of a virtual scene, having a predetermined surface at a position within that scene; providing the position and a direction based on the viewpoint to a machine learning system previously trained to predict a factor that, when combined with a distribution function that characterises an interaction of light with the predetermined surface, generates a pixel value corresponding to the first element of the virtual scene as illuminated at the position, combining the predicted factor from the machine learning system with the distribution function to generate the pixel value corresponding to the illuminated first element of the virtual scene at the position, and incorporating the pixel value into a rendered image for display, where the machine learning system was previously trained with a training set based on images comprising multiple lighting conditions.

Image rendering method and apparatus

An image rendering method for rendering a pixel of a virtual scene at a viewpoint includes: downloading a machine learning system corresponding to a current or anticipated state an application determining the virtual scene to be rendered from among a plurality of machine learning systems corresponding to a plurality of states of the application; providing a position and a direction based on the viewpoint to the machine learning system previously trained to predict a factor; combining the predicted factor from the machine learning system with a distribution function that characterises an interaction of light with a predetermined surface to generate the pixel value corresponding to an illuminated first element of the virtual scene at the position; and incorporating the pixel value into a rendered image for display.

A content modification system comprising a content receiving unit operable to receive content comprising a virtual environment and one or more active elements, an input receiving unit operable to receive inputs from one or more users, an element addition unit operable to generate one or more virtual elements within the virtual environment in response to the received inputs, the virtual elements being unable to be interacted with by the one or more active elements, and a content generation unit operable to generate modified content comprising the virtual environment, the one or more active elements, and the one or more generated virtual elements.

Content enhancement system and method

A system for generating video game inputs is provided. The system comprises an input unit operable to obtain images of a passive non-luminous object being held by a user as a video games controller. The system also comprises an object detector and object pose detector for detecting the object and its respective pose in the obtained images. The pose detector is configured to detect the pose of the object based on at least one of a (i) contour detection operation and (ii) the output of a machine learning model that has been trained to detect the poses of passive non-luminous objects in images. A user input generator is configured to generate user inputs based on the detected changes in pose of the passive non-luminous object and to transmit these to a video game unit at which a video game is being executed. A corresponding method is also provided.

System and method for generating user inputs for a video game

A data processing apparatus adapted to output recommendation information for modifying source code, includes: compiler circuitry to compile the source code and to output compiled code for the source code, processing circuitry to execute the compiled code, profile circuitry to monitor the execution of the compiled code by the processing circuitry and to generate profile information for the execution of the compiled code, the profile information including one or more statistical properties for the execution of the compiled code, and recommendation circuitry to output the recommendation information for the source code, the recommendation circuitry including a machine learning model to receive at least a portion of the profile information and trained to output the recommendation information for the source code in dependence upon one or more of the statistical properties, in which the recommendation information is indicative of one or more editing instructions for modifying the source code.

Apparatus and method for source code optimisation

A content generation system, the system comprising an input obtaining unit operable to obtain one or more samples of input text and/or audio relating to a first content, an input analysis unit operable to generate n-grams representing one or more elements of the obtained inputs, a representation generating unit operable to generate a visual representation of one or more of the generated n-grams, and a display generation unit operable to generate second content comprising one or more elements of the visual representation in association with the first content.

Content generation system and method

A data processing apparatus includes input circuitry to receive viewpoint data indicative of respective viewpoints for a plurality of spectators of a virtual environment, detection circuitry to detect a portion of the virtual environment viewed by each of the respective viewpoints in dependence upon the viewpoint data, selection circuitry to select one or more regions of the virtual environment in dependence upon at least some of the detected portions, and output circuitry to output data indicative of one or more of the selected regions.

Apparatus and method for data aggregation

A method of capturing free viewpoint content at a location includes recording video on each of a plurality of portable video recording devices at the location; each portable video recording device detecting a wireless synchronisation signal transmitted at the location; and each portable video recording device periodically adding a timestamp to its respective recorded video; where the timestamp is responsive to the detected wireless synchronisation signal, thereby enabling synchronisation of a plurality of recorded videos responsive to the timestamps.

Image acquisition system and method

A system for characterising surfaces in a real-world scene, the system comprising an object identification unit operable to identify one or more objects within one or more captured images of the real-world scene, a characteristic identification unit operable to identify one or more characteristics of one or more surfaces of the identified objects, and an information generation unit operable to generate information linking an object and one or more surface characteristics associated with that object.

Surface characterisation apparatus and system

A method of obtaining real world scale information for a scene includes obtaining at least one image of a plurality of objects in a scene; detecting at least some of the objects in the at least one image as corresponding to pre-determined objects; generating a 3D reconstruction of the scene based on the image content of the at least one image; determining a relative size of each object in the 3D reconstruction of the scene in at least one dimension, the relative size being defined in dimensions of the generated 3D reconstruction; where the relative size of each object is determined based on a distance between at least two points corresponding to that object as transformed into 3D space; obtaining a size probability distribution function for each object detected in the at least one image, each size probability distribution function defining a range of sizes in at least one dimension that a corresponding object is likely to possess in real world units; resealing the size probability distribution function for each detected object based on a corresponding relative size of that object in the 3D reconstruction; and estimating a geometry of the scene in real world units by combining the re-scaled probability distribution function for at least one detected object with the re-scaled probability distribution function for at least one other detected object.

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London, United Kingdom

Timothy Edward Bradley