Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

• URL: http://arxiv.org/abs/2104.13710v1
• Date: Wed, 28 Apr 2021 11:31:35 GMT
• Title: Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry
• Authors: Oussema Bouafif, Bogdan Khomutenko, Mohamed Daoudi
• Abstract summary: We present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only.
• Score: 3.970492757288025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

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