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 thes...

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Veröffentlicht in:	Intelligent Systems and Computer Vision (Online) S. 1 - 8
Hauptverfasser:	Batarfi, Mahfoudh M., Mareboyana, Manohar
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 08.05.2024
Schlagworte:	Computational modeling Computer architecture Face recognition Graph Convolutional Networks (GCNs) Graph Neural Networks (GNNs) Hyperparameter optimization Loss measurement Optimization Hyperparameter 3D-face Reconstruction Solid modeling Three-dimensional displays Variation Graph Autoencoder (VGAE)
ISSN:	2768-0754
Online-Zugang:	Volltext
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