Transformer
In short: A Transformer is a neural network architecture based on self-attention that has become the foundation for state-of-the-art lip sync models due to its ability to capture long-range dependencies.
About Transformer
The Transformer architecture, originally introduced for natural language processing, uses self-attention to process sequences without the sequential bottleneck of recurrent networks. In lip sync, Transformers can attend to the entire audio sequence simultaneously, capturing long-range dependencies like sentence-level prosody that influence mouth movements.
They also enable parallel processing during training, making it feasible to train on larger datasets. Recent lip sync models increasingly use Transformer components for both audio encoding and video generation, often combined with diffusion or GAN-based decoders for the final pixel generation step.
How Transformer Connects to Lip Sync
Transformer relates to several other concepts in the AI lip sync pipeline: Attention Mechanism , and Encoder-Decoder .
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