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Found 4 papers, 2 papers with code
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection
Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset.
DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis
The rendering view is then manipulated with a novel conditional control module that interprets the camera pose by watching a condition image of a crossed subject from the same view.
DisUnknown: Distilling Unknown Factors for Disentanglement Learning
In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor.
One-Shot Identity-Preserving Portrait Reenactment
This is achieved by a novel landmark disentanglement network (LD-Net), which predicts personalized facial landmarks that combine the identity of the target with expressions and poses from a different subject.
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