Pose and Facial Expression Transfer by using StyleGAN
Petr Jahoda, Jan Cech·April 17, 2025
Summary
A method using StyleGAN2 was introduced at the 27th Computer Vision Winter Workshop in Terme Olimia, Slovenia. This method allows for real-time transfer of pose and expression between face images, using two encoders and a mapping network to project inputs into the latent space. Self-supervised training from video sequences enables synthesis of random identities without manual labeling, achieving near-real-time performance and generalizing to paintings. This approach offers a simpler, more efficient alternative compared to previous methods, highlighting the flexibility and control of the proposed model.
Introduction
Background
Overview of StyleGAN2 and its capabilities
Importance of real-time face pose and expression transfer in computer vision
Objective
To introduce a novel method for real-time transfer of pose and expression between face images
Highlight the use of StyleGAN2 in achieving this objective
Method
Data Collection
Source of face images for training and testing
Video sequences for self-supervised training
Data Preprocessing
Preparation of face images for input into the model
Techniques for video sequence analysis and extraction of pose and expression data
Model Architecture
Description of the two encoders and mapping network
Explanation of how inputs are projected into the latent space
Training Process
Self-supervised training methodology
Explanation of how the model learns to transfer pose and expression without manual labeling
Performance Evaluation
Metrics used to assess the model's accuracy and efficiency
Comparison with previous methods in terms of real-time performance and generalization capabilities
Results
Real-time Performance
Demonstration of the model's ability to perform real-time face pose and expression transfer
Generalization to Paintings
Case studies showing the model's application to paintings and other non-real images
Efficiency and Simplicity
Comparison of the proposed method with previous approaches in terms of computational resources and complexity
Flexibility and Control
Discussion on the model's adaptability to different scenarios and the level of control it offers to users
Conclusion
Summary of Contributions
Recap of the method's innovations and improvements over existing techniques
Future Work
Potential areas for further research and development
Impact and Applications
Discussion on the broader implications of the method in fields such as entertainment, education, and healthcare
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
How does the proposed method generalize to different types of images, such as paintings, compared to previous methods?
How do the two encoders and mapping network function together in the StyleGAN2 method for pose and expression transfer?
What are the key implementation steps for achieving real-time performance in the proposed StyleGAN2 method?
What are the main innovative aspects of using self-supervised training in the StyleGAN2 method?