Synthetic Face
Synthetic face generation is a rapidly evolving field focused on creating realistic artificial faces for various applications, primarily driven by privacy concerns surrounding the use of real facial data in areas like face recognition. Current research emphasizes developing generative models, particularly diffusion models and GANs, that produce highly diverse and identity-consistent synthetic faces while mitigating biases and improving generalization to real-world images. This work is crucial for advancing privacy-preserving technologies in face recognition and other biometric applications, as well as providing valuable tools for studying algorithmic bias and human perception of facial features.
Papers
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
Hatef Otroshi Shahreza, Anjith George, Sébastien Marcel
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
Yang Liu, Cheng Yu, Lei Shang, Yongyi He, Ziheng Wu, Xingjun Wang, Chao Xu, Haoyu Xie, Weida Wang, Yuze Zhao, Lin Zhu, Chen Cheng, Weitao Chen, Yuan Yao, Wenmeng Zhou, Jiaqi Xu, Qiang Wang, Yingda Chen, Xuansong Xie, Baigui Sun