Dynamic Neural Face Morphing for Visual Effects

Abstract

In this work we present a machine learning approach for face morphing in videos, between two or more identities. We devise an autoencoder architecture with distinct decoders for each identity, but with an underlying learnable linear basis for their weights. Each decoder has a learnable parameter that defines the interpolating weights (or ID weights) for the basis which can successfully decode its identity. During inference, the ID weights can be interpolated to produce a range of morphing identities. Our method produces temporally consistent results and allows blending different aspects of the identities by exposing the blending weights for each layer of the decoder network. We deploy our trained models to image compositors as 2D nodes with independent controls for the blending weights. Our approach has been successfully used in production, for the aging of David Beckham in the Malaria Must Die campaign.