Sri International

Blueskeye Ai Ltd

Timur Almaev

Amir Tamrakar

Mohamed R Amer

Timothy J Shields

Anthony Brown

Michel Valstar

Max Ehrlich

A computing system comprising a generative neural network () is disclosed. The generative neural network () is configured to receive a plurality of input videos. Each input video comprises a face defining an identity. Each input video comprises a behaviour. Each input video comprises the same behaviour as each of the other input videos, and a different identity to each of the other input videos. The generative neural network () is also configured synthesize an output video from the input videos. The output video comprises a synthetic face defining a synthetic identity. The generative neural network has been trained, with a loss function, to preserve the behaviour of the input videos while generating the synthetic identity that is different from each identity of the input videos.

Generative neural network for synthesis of faces and behaviors

Technologies for analyzing multi-task multimodal data to detect multi-task multimodal events using a deep multi-task representation learning, are disclosed. A combined model with both generative and discriminative aspects is used to share information during both generative and discriminative processes. The technologies can be used to classify data and also to generate data from classification events. The data can then be used to morph data into a desired classification event.

Deep multi-task representation learning

Growing community of inventors

Nottingham, United Kingdom

Timur Almaev