HFM-GS: Half-Face Mapping 3DGS Avatar Based Real-Time HMD Removal

In extended reality (XR) applications, enhancing user perception often necessitates head-mounted display (HMD) removal. However, existing methods suffer from low time performance and suboptimal reconstruction quality. In this paper, we propose a half face mapping 3D Gaussian splatting avatar based HMD removal method (HFM-GS), which can perform real-time and high-fidelity online restoration of the complete face in HMD-occluded videos for XR applications after a short un-occluded face registration. We establish a mapping field between the upper and lower face Gaussians to enhance the adaptability to deformation. Then, we introduce correlation weight-based sampling to improve time performance and handle variations in the number of Gaussians. At last, we ensure model robustness through Gaussian Segregation Strategy. Compared to two state-of-the-art methods, our method achieves better quality and time performance. The results of the user study show that fidelity is significantly improved with our method.