  Existing automated dubbing methods are usually designed for Professionally
Generated Content (PGC) production, which requires massive training data and
training time to learn a person-specific audio-video mapping. In this paper, we
investigate an audio-driven dubbing method that is more feasible for User
Generated Content (UGC) production. There are two unique challenges to design a
method for UGC: 1) the appearances of speakers are diverse and arbitrary as the
method needs to generalize across users; 2) the available video data of one
speaker are very limited. In order to tackle the above challenges, we first
introduce a new Style Translation Network to integrate the speaking style of
the target and the speaking content of the source via a cross-modal AdaIN
module. It enables our model to quickly adapt to a new speaker. Then, we
further develop a semi-parametric video renderer, which takes full advantage of
the limited training data of the unseen speaker via a video-level
retrieve-warp-refine pipeline. Finally, we propose a temporal regularization
for the semi-parametric renderer, generating more continuous videos. Extensive
experiments show that our method generates videos that accurately preserve
various speaking styles, yet with considerably lower amount of training data
and training time in comparison to existing methods. Besides, our method
achieves a faster testing speed than most recent methods.
