A text-only talking face generator maps text to visemes, hallucinates pseudo-audio, and renders lip-synced video; the paper's headline metrics are partially contradicted by its own tables.
Combining Residual Networks with LSTMs for Lipreading
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abstract
We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size target-words consisting of 1.28sec video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0, yielding 6.8 absolute improvement over the current state-of-the-art, without using information about word boundaries during training or testing.
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Text2Lip: Progressive Lip-Synced Talking Face Generation from Text via Viseme-Guided Rendering
A text-only talking face generator maps text to visemes, hallucinates pseudo-audio, and renders lip-synced video; the paper's headline metrics are partially contradicted by its own tables.