TT-SAC is a parameter-free inference framework that uses a generator-encoder feedback loop to adapt conditioning representations and stabilize identity and motion in audio-driven talking-head videos.
Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks
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abstract
We present a novel approach to generating photo-realistic images of a face with accurate lip sync, given an audio input. By using a recurrent neural network, we achieved mouth landmarks based on audio features. We exploited the power of conditional generative adversarial networks to produce highly-realistic face conditioned on a set of landmarks. These two networks together are capable of producing a sequence of natural faces in sync with an input audio track.
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Test-Time Self-Adaptive Conditioning for Stable Audio-Driven Talking-Head Generation
TT-SAC is a parameter-free inference framework that uses a generator-encoder feedback loop to adapt conditioning representations and stabilize identity and motion in audio-driven talking-head videos.