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Found 4 papers, 3 papers with code
One-shot Face Reenactment
However, in real-world scenario end-users often only have one target face at hand, rendering existing methods inapplicable.
CMR motion artifact correction using generative adversarial nets
Boosted by deep learning and adversarial training algorithm, our model is trainable in an end-to-end manner, can be tested in real-time and achieves the state-of-art results for CMR correction.
ReenactGAN: Learning to Reenact Faces via Boundary Transfer
A transformer is subsequently used to adapt the boundary of source face to the boundary of target face.
Quantifying Facial Age by Posterior of Age Comparisons
We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels.
Ranked #7 on
Age Estimation
on MORPH Album2
(using extra training data)
