Image synthesis method comprising using a neural encoder and plurality of neural decoders that each correspond to a respective persona to: receive image data that includes a target object and source data that identifies one of the personas; map the image data to a latent space representation of the image data using the neural encoder; identify one of the neural decoders based on the persona identified in the source data and use the identified neural decoder to decode the latent space representation of the image data so that the target object is swapped with the identified persona, thus producing swapped image data; blend the swapped image data with the target object in the image data to produce a synthesised image. Performances may be transferred from one actor to another character. Source facial appearances of the personas may be swapped with the face of a target performer. Physical characteristics of the source character (eg. voice, build, facial features) may be mapped to a target behavioural performance. Facial landmarks of faces detected in the target image may be localised, then rotated and scaled to enable face alignment. The method employs a deep learning model implemented using a comb neural network architecture.
Automated image synthesis using a comb neural network architecture