Privacy preserving for AI-based 3D human pose recovery and retargeting

Abstract

As an essential research task in artificial intelligence (AI), the estimation of 3D human poses has important application value in virtual reality, medical diagnosis, athlete training and other fields. However, human pose recovery and retargeting require the acquisition of detailed visual data containing private information, which has led to increasing concerns about user privacy and security. Therefore, we build a lightweight framework, called Human Motion Parameters Prediction (HMPP), which can infer the 3D mesh and 3D skeletal joint points of the human body while protecting the privacy of the user. The proposed method successfully reduces or suppresses privacy attributes while ensuring important features to perform human pose estimation. The 2D and 3D joints are used for supervision to improve the interpretability of the model at each stage. In addition, the prediction of the camera’s internal parameters is added so that the model can be augmented with projection supervision, thereby using more 2D datasets for training and improving the generalization ability of the model. Finally, the predicted motion parameters are used for 3D reconstruction and motion retargeting. Experiments show that our approach can achieve excellent evaluation results on multiple datasets and avoid inadvertently compromising private and sensitive data.