Abstract: Techniques are described for image generation for avatar image animation using translation vectors. An avatar image is obtained for representation on a first computing device. An autoencoder is trained, on a second computing device comprising an artificial neural network, to generate synthetic emotive faces. A plurality of translation vectors is identified corresponding to a plurality of emotion metrics, based on the training. A bottleneck layer within the autoencoder is used to identify the plurality of translation vectors. A subset of the plurality of translation vectors is applied to the avatar image, wherein the subset represents an emotion metric input. The emotion metric input is obtained from facial analysis of an individual. An animated avatar image is generated for the first computing device, based on the applying, wherein the animated avatar image is reflective of the emotion metric input and the avatar image includes vocalizations.

Abstract: Techniques are described for image generation for avatar image animation using translation vectors. An avatar image is obtained for representation on a first computing device. An autoencoder is trained, on a second computing device comprising an artificial neural network, to generate synthetic emotive faces. A plurality of translation vectors is identified corresponding to a plurality of emotion metrics, based on the training. A bottleneck layer within the autoencoder is used to identify the plurality of translation vectors. A subset of the plurality of translation vectors is applied to the avatar image, wherein the subset represents an emotion metric input. The emotion metric input is obtained from facial analysis of an individual. An animated avatar image is generated for the first computing device, based on the applying, wherein the animated avatar image is reflective of the emotion metric input and the avatar image includes vocalizations.