A computer implemented method for generating, via a generator neural network comprising an encoder neural network and an attention neural network, a sequence of images representing a temporal progression, the method comprising: encoding, via the encoder neural network, a set of latent values across a series of time steps to produce a feature map for each time step, each feature map relating to a corresponding output image for the time step and encoding information relating to a time dimension, a height dimension and a width dimension; applying the attention neural network to the feature maps to determine the influence of each location in each feature map on each position in each image, the attention neural network comprising: a height attention layer configured to determine a height attention map by applying attention to all feature maps across the height dimension; a width attention layer configured to, for each time step, determine a width attention map by applying attention to all feature maps across the width dimension; and a time attention layer configured to, for each time step, determine a time attention map by applying attention to all feature maps across the time dimension, wherein the generator neural network determines, for each time step, an image for the time step by applying the height, width and time attention maps to a decoding of the feature maps. encoding, via the encoder neural network, a set of latent values across a series of time steps to produce a feature map for each time step, each feature map relating to a corresponding output image for the time step and encoding information relating to a time dimension, a height dimension and a width dimension; applying the attention neural network to the feature maps to determine the influence of each location in each feature map on each position in each image, the attention neural network comprising: a height attention layer configured to determine a height attention map by applying attention to all feature maps across the height dimension; a width attention layer configured to, for each time step, determine a width attention map by applying attention to all feature maps across the width dimension; and a time attention layer configured to, for each time step, determine a time attention map by applying attention to all feature maps across the time dimension, wherein the generator neural network determines, for each time step, an image for the time step by applying the height, width and time attention maps to a decoding of the feature maps. a height attention layer configured to determine a height attention map by applying attention to all feature maps across the height dimension; a width attention layer configured to, for each time step, determine a width attention map by applying attention to all feature maps across the width dimension; and a time attention layer configured to, for each time step, determine a time attention map by applying attention to all feature maps across the time dimension, wherein the generator neural network determines, for each time step, an image for the time step by applying the height, width and time attention maps to a decoding of the feature maps. <EOS>