TeleGS: End-to-End Monocular Gaussian Head for Immersive Telepresence
Zipeng Pan, Yuan Zhang, Tao Lin · 2025
Current immersive telepresence systems face significant deployment barriers due to prohibitive hardware costs and stringent environmental requirements. To address these challenges, we propose TeleGS, a novel monocular 3D head reconstruction framework that synergizes 3D-GAN priors with 3D Gaussian Splatting (3DGS), offering the potential for immersive telepresence on accessible singlecamera consumer-grade hardware. Specifically, we design a fast initialization process that directly generates 3D Gaussians from 3D-GAN features and introduce a decoupled appearance model that combines view-independent features from 3D-GAN priors with view-dependent color prediction to achieve high-fidelity rendering. Furthermore, we implemented an end-to-end pipeline by integrating 3D-GAN inversion. Our system achieves 30× faster rendering and 1.22dB PSNR gain over the best-performing baseline, paving the way for practical, real-time 3D telepresence applications on consumer hardware.