Face to Face Translation
4 papers with code • 0 benchmarks • 0 datasets
Given a video of a person speaking in a source language, generate a video of the same person speaking in a target language.
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The geometry alignment is performed pixel-wise, i. e., every pixel of the face is corresponded to a pixel of the reference face.
We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i. e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style.
To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose.