Optimization of Graph Convolutional Networks with Variational Graph Autoencoder Architecture for 3D Face Reconstruction Task

The study addresses the fundamental challenges encountered in 3D face reconstruction, including the inadequacy of dedicated research initiatives, the complexity of hyperparameter optimization, the lack of accessible guidance for newcomers, and the dependency on low-quality datasets. To surmount these obstacles, the study embarks on the optimization of a Variational Graph Autoencoder (VGAE)-based architecture enriched with five distinct Graph Convolutional Network (GCN) layers, namely GCNConv, SGConv, SSGConv, GraphConv, and Chebyshev. The primary objective is twofold: first, to curate a diverse and high-fidelity 3D face dataset capable of meeting the criteria of this study posed by large-scale data intricacies, and second, to refine VGAE architectures through the strategic integration of these GCN layers. This integration aims to bolster the reconstruction capabilities of VGAE architectures and ascertain the optimal GCN layer type for maximizing accuracy (by minimizing validation loss) and computational efficiency (measured in execution time). Furthermore, the study provides comprehensive guidance tailored for researchers navigating the complexities of 3D face reconstruction. Through these concerted efforts, the findings of this research empower researchers to tackle challenging tasks such as facial analysis and reconstruction across various domains. The study endeavors to propel the field of 3D face reconstruction forward, offering valuable insights and methodologies to enhance the efficacy of VGAE architectures across diverse applications and domains. Moreover, the techniques and knowledge derived from this study can contribute to developing robust and efficient approaches for Generative AI applications beyond 3D face mesh reconstruction.