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

Wenjun Yang; Geguo Du

doi:10.1117/12.3021549

19 February 2024 Deep learning-based 3D face reconstruction method for video stream

Wenjun Yang, Geguo Du

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130630H (2024) https://doi.org/10.1117/12.3021549
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenjun Yang and Geguo Du "Deep learning-based 3D face reconstruction method for video stream", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130630H (19 February 2024); https://doi.org/10.1117/12.3021549

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