Identity-Obscured Neural Radiance Fields: Privacy-Preserving 3D Facial
  Reconstruction
Jiayi Kong Baixin Xu Xurui Song Chen Qian Jun Luo Ying He
Neural radiance fields (NeRF) typically require a complete set of images
taken from multiple camera perspectives to accurately reconstruct geometric
details. However, this approach raise significant privacy concerns in the
context of facial reconstruction. The critical need for privacy protection
often leads invidividuals to be reluctant in sharing their facial images, due
to fears of potential misuse or security risks. Addressing these concerns, we
propose a method that leverages privacy-preserving images for reconstructing 3D
head geometry within the NeRF framework. Our method stands apart from
traditional facial reconstruction techniques as it does not depend on RGB
information from images containing sensitive facial data. Instead, it
effectively generates plausible facial geometry using a series of
identity-obscured inputs, thereby protecting facial privacy.