Abstract: A method of point cloud generation comprises generating, by a processor, a first point cloud comprising a first set of generated data points; calculating, by the processor, a loss function representative of the quality of the first point cloud, the loss function comprising a smoothness term obtained by constructing vectors between neighbouring data points in the point cloud, performing vector multiplication between neighbouring pairs of vectors, and outputting a smoothness term based on the result of the vector multiplication; and generating, by the processor, a second point cloud comprising a second set of generated data points based at least in part on the calculated loss function of the first point cloud.

Abstract: A data processing apparatus for detecting one or more errors for one or more video images for a video game, the data processing apparatus comprising receiving circuitry to receive a sequence of video images for the video game and one or more action inputs associated with the sequence of video images, prediction circuitry to generate a predicted video image in dependence on at least one video image of the sequence of video images and an action input associated with the at least one video image, the prediction circuitry comprising a trained machine learning model to generate the predicted video image, and error detection circuitry to detect, for one or more video images subsequent to the at least one video image, one or more errors in dependence on the predicted video image.

Abstract: A data processing apparatus comprising circuitry configured to: receive data indicating an audio sample captured in a physical environment of a player of a video game; determine a classification of the audio sample; determine an in-game occurrence associated with the determined classification of the audio sample; and control the determined in-game occurrence to be executed in the video game.

Abstract: A data processing apparatus for determining description data for describing content includes: a video captioning model to receive an input comprising at least video images associated with the content, wherein the video captioning model is trained to detect one or more predetermined motions of one or more animated objects in the video images and determine one or more captions in dependence on one or more of the predetermined motions, one or more of the captions comprising respective caption data comprising one or more words for describing one or more of the predetermined motions, the respective caption data comprising one or more of audio data, text data and image data; and output circuitry to output description data in dependence on one or more of the captions.

Abstract: An apparatus for generating datasets for training machine learning models includes: a receiving unit configured to receive video data comprising sequential image frames; a storage unit configured to store a plurality of the sequential image frames; and a selecting unit configured to select, for a target image frame, a subset of stored image frames, the subset providing contextual data relating to the target image frame for the machine learning model. The selecting unit is configured to successively generate sampling values, wherein a difference between successive sampling values increases with each successively generated sampling value; and the selecting unit is configured to select a given image frame from the stored image frames in dependence upon whether a number of sequential image frames between the given image frame and the target image frame coincides with one of the successively generated sampling values.

Abstract: A method of point cloud generation comprises generating, by a processor, a first point cloud comprising a first set of generated data points; calculating, by the processor, a loss function representative of the quality of the first point cloud, the loss function comprising a smoothness term obtained by constructing vectors between neighbouring data points in the point cloud, performing vector multiplication between neighbouring pairs of vectors, and outputting a smoothness term based on the result of the vector multiplication; and generating, by the processor, a second point cloud comprising a second set of generated data points based at least in part on the calculated loss function of the first point cloud.

Abstract: A system for generating a training dataset for a machine learning process, and training a machine learning model, the system comprising a data obtaining unit configured to obtain training data comprising a plurality of events of interest and the behaviour of an agent corresponding to those events, an event identifying unit configured to identify, based upon one or more corresponding indicators, the occurrence of an event of interest in the training data, a list generating unit configured to generate a list of identified events in the training data, wherein identified events are added to the list with a probability that is inversely proportional to the frequency of the occurrence of that event within the training data, a dataset generating unit configured to generate a dataset comprising information about the events contained in the generated list, and a training unit configured to train a machine learning model using the generated dataset, wherein the machine learning model is trained to generate behaviour fo