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Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed controllable generator instead of ConvLSTM | Facial expression video generation · Web test faces, mean across Happy, Surprise, and Angry | AMT preference: 83.0% | Helped |
| Use the proposed controllable generator instead of HP | Facial expression video generation · Web test faces, mean across Happy, Surprise, and Angry | AMT preference: 71.4% | Helped |
| Use the proposed method rather than either baseline | Facial expression video generation · Web test faces, mean across expressions | AMT preference: 65.1% | Helped |
| Add a local discriminator for the mouth region (ablation) | Proposed facial expression generator · Facial expression videos | Visual quality | Helped |
| Add landmark prediction (ablation) | Proposed facial expression generator · Facial expression videos | Visual clarity | Helped |
| Add temporal continuity regularization (ablation) | Proposed facial expression generator · Facial expression videos | Temporal continuity | Helped |
| Train one joint model for multiple expressions instead of separate models | Facial expression generator · Happy and Angry expression generation | Visual realism | Helped |
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