Deep learning-based 3D face reconstruction method for video stream

Face is an important source of information for interpersonal communication and recognition, so facial reconstruction technology has always been a research focus in the field of computer vision. We present a deep learning-based algorithm for accurate three-dimensional face reconstruction from a single-view video stream. The proposed method processes the input video stream frame by frame, extracts facial region information, and constructs a facial reconstruction network that utilizes 3D Morphable Models to reconstruct the precise geometric shape of the face. Additionally, we design a multitiered loss function, including low-level pixel consistency loss, facial landmark loss, and high-level identity loss. Furthermore, these multi-tiered losses are utilized as weak supervision signals to guide the supervised learning of the reconstructed face, thereby enhancing the quality and accuracy of the reconstruction.