AgeSynthGAN: Advanced Facial Age Synthesis with StyleGAN2
Tung-Ke Hsieh, Tsung-Jung Liu, Kuan-Hsien Liu · 2024
Facial age synthesis is an important research area that aims to synthesize facial images from the past (age progression) or the future (age regression) by reflecting the age factors of a given face. Ideally, this task should be able to synthesize natural faces of different ages while maintaining identity consistency. However, existing methods suffer from background blur and inconsistency issues when processing the generated images. In this study, we propose a new method that utilizes semantic segmentation technology and attention mechanisms to solve these problems. Through this method, we can control the background of the generated image, making it clearer and more consistent, and we added an attention mechanism to better extract the features of faces. Additionally, we introduce a shape loss to simulate the shape changes of faces across different ages. We evaluate our approach through both qualitative and quantitative assessments. The results show that, compared with state-of-the-art methods, our approach is visually superior, achieves higher age accuracy, and provides reasonable identity confidence. Overall, our research presents a new method for facial age synthesis with promising applications and theoretical significance. The source code and model are available at https://reurl.cc/34QX5O.