Sparse semantic disentangled face attribute editing

Abstract

The technology described herein provides an improved framework for a face editing task performed by a machine-learning model. The technology provides a self-training strategy aimed at achieving more robust and generalizable face video editing. The self-training strategy helps overcome a shortage of training data relevant to the face editing task. The technology also provides a semantically disentangled architecture capable of catering to a diverse range of editing requirements. The technology also provides sparse learning to avoid over editing. The sparse learning technology partitions the model being trained according to facial regions being edited. This strategy teaches the model to transform only the most pertinent facial areas for a specific task. For example, when changing the eyebrows on a face the eye area will change, but the mouth area should remain unchanged.