Neural Style-Preserving Visual Dubbing

TL;DR

This paper introduces a style-preserving visual dubbing method that modifies facial expressions in target videos to match foreign languages while maintaining individual actor styles, using a recurrent GAN trained unsupervisedly.

Contribution

It presents a novel approach that captures and preserves actor-specific expression styles during dubbing, improving realism over previous methods.

Findings

Maintains target actor's style better than previous methods

Generates temporally coherent and photorealistic videos

Handles dynamic backgrounds effectively

Abstract

Dubbing is a technique for translating video content from one language to another. However, state-of-the-art visual dubbing techniques directly copy facial expressions from source to target actors without considering identity-specific idiosyncrasies such as a unique type of smile. We present a style-preserving visual dubbing approach from single video inputs, which maintains the signature style of target actors when modifying facial expressions, including mouth motions, to match foreign languages. At the heart of our approach is the concept of motion style, in particular for facial expressions, i.e., the person-specific expression change that is yet another essential factor beyond visual accuracy in face editing applications. Our method is based on a recurrent generative adversarial network that captures the spatiotemporal co-activation of facial expressions, and enables generating and modifying the facial expressions of the target actor while preserving their style. We train our model with unsynchronized source and target videos in an unsupervised manner using cycle-consistency and mouth expression losses, and synthesize photorealistic video frames using a layered neural face renderer. Our approach generates temporally coherent results, and handles dynamic backgrounds. Our results show that our dubbing approach maintains the idiosyncratic style of the target actor better than previous approaches, even for widely differing source and target actors.