Research map / Video / Video generation
PVL: A Framework for Navigating the Precision-Variety Trade-Off in Automated Animation of Smiles
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase PVL parameter m (more positive training samples) (ablation) | KNN classifier · synthetic circular boundary | variety | Helped |
| increase PVL parameter m (ablation) | KNN classifier · synthetic circular boundary | precision | Hurt |
| increase m on real face data (ablation) | KNN classifier · two-class face data | precision | Hurt |
| use SVM instead of KNN under PVL | SVM · face data specificity-variety | monotonicity | Mixed |
| use random forest instead of KNN under PVL | RF · face data specificity-variety | monotonicity | Mixed |
| generate with m=8 versus m=3 (ablation) | PVL KNN · pairwise similarity ratings | perceived variety: Z = 4.78 | Helped |
| predicted Full class versus predicted Partial class smiles | PVL KNN m=8 · 2AFC happiness | probability judged happier: 0.68 | Helped |
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