3D Facial Reconstruction from a Single Image Using a Hybrid Model Based on 3DMM and Deep Learning

A fundamental challenge in computer vision is accurately modelling 3D faces from images. It facilitates the creation of immersive virtual experiences, realistic facial animations, and reliable identity verification. This research introduces an innovative approach aimed at reconstructing intricate facial attributes, encompassing shape, pose, and expression from a single input image. The proposed methodology employs a fusion of two potent techniques: 3D Morphable Models (3DMMs) and advanced Deep Learning (DL) methodologies. By integrating DL into tasks like face detection, expression analysis, and landmark extraction, the framework excels in reconstructing realistic facial attributes from single images even in diverse environments. The framework achieves compelling results in reconstructing "in-the-wild" faces, exhibiting notable fidelity while preserving essential facial characteristics. Experimental evaluations confirm the effectiveness and robustness of our approach, confirming its adaptability across various scenarios. Our research contributes to the advancement of 3Dface modelling techniques, addressing the challenges of accurate reconstruction and holding promise for applications in virtual reality, facial animation, medical, security, and biometrics.