PixelSmile: Toward Fine-Grained Facial Expression Editing

1. 1Construct a dataset like Flex Facial Expression (FFE) with continuous affective annotations
2. 2Establish a benchmark like FFE-Bench to evaluate editing performance
3. 3Implement PixelSmile, a diffusion framework that disentangles expression semantics
4. 4Fine-tune PixelSmile on the FFE dataset to achieve high editing accuracy and identity preservation

Computer vision engineers and researchers on a team can benefit from PixelSmile to improve facial expression editing, while product managers can consider its applications in various industries

💡 PixelSmile uses a diffusion framework to disentangle expression semantics for accurate facial expression editing

🤖 Fine-grained facial expression editing with PixelSmile! 📸