Deep Insights into 3D Face Reconstruction from Blurred 2D Inputs: A Comprehensive Framework
Computer Vision; Convolutional Neural Network; Deep Learning; Generative Adversarial Network
This framework outlines a multi-stage methodology for 3D face reconstruction driven by advancements in deep learning. The process involves image preprocessing with deblurring techniques and subsequent feature extraction using CNNs alongside traditional methods. Deep learning adapts to diverse image challenges, ensuring accuracy in 3D reconstructions. In medical imaging, the proficiency of 3D CNNs and GANs shines in extracting structures from MRI and CT scans. Post-processing steps encompass mesh smoothing and texture mapping for enhanced visual quality. Evaluation metrics (MAE, RMSE, IoU) guarantee the precision of depth estimations. Applications of deep learning span across CNNs, 3DMM, GANs, and networks for landmark detection and dense correspondence. Challenges include optimizing eye reconstruction, expanding applications, and addressing concerns related to data quality, privacy, and hardware requirements. 2024 IEEE.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Institute of Electrical and Electronics Engineers Inc.
Pastay S., Christ Deemed to Be University, Department of Data Science, Lavasa, Pune, India; Joseph A., Christ Deemed to Be University, Department of Data Science, Lavasa, Pune, India; Kumar R., Christ Deemed to Be University, Department of Data Science, Lavasa, Pune, India