Encoder-Decoder
In short: An encoder-decoder is a neural network architecture that compresses input data into a compact representation and then reconstructs output from it, widely used in lip sync model design.
About Encoder-Decoder
Encoder-decoder architectures consist of two main components: an encoder that processes input (audio features, face images) into a compact latent representation, and a decoder that generates output (modified face frames) from that representation. In lip sync models, the encoder typically processes audio features into speech representations while a separate encoder processes the face image.
These representations are then combined and fed to the decoder, which generates the lip-synced output frame. This architecture allows the model to learn efficient representations of both audio and visual information, making it a standard building block in lip sync system design.
How Encoder-Decoder Connects to Lip Sync
Encoder-Decoder relates to several other concepts in the AI lip sync pipeline: Latent Space , and Transformer .
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