3D face image reconstruction using multi-scale dense 3D convolutional generative networks with axial spatial attention for personalized avatar generation

A 3D face reconstruction approach using advanced deep learning techniques is implemented to create avatars. At first, 2D face images are obtained from standard repositories. These images are given to the proposed deep learning technique called Adaptive Multi-Scale Dense 3D Convolutional Generative Adversarial Network with Axial Spatial Attention (AMD-3DCGAN-SA) model for 3D face reconstruction. The 3DCNN model effectively reconstructs facial features besides 3D face imagery. Global spatial features in face images are precisely analyzed by the axial spatial attention mechanism to preserve all the important facial details. Furthermore, an optimization algorithm known as Fitness Normalization-based Willow Catkin Optimization (FNWCO) is used for fine-tuning model parameters for accurate 3D facial modelling. Overall, the effectiveness of the developed approach was evaluated against traditional face reconstruction methods to ensure its efficiency.