Authors:
Mohamed Dhouioui
1
;
Tarek Frikha
1
;
Hassen Drira
2
and
Mohamed Abid
1
Affiliations:
1
CES-Lab, ENIS, University of Sfax, Sfax, Tunisia
;
2
Centre e Recherche en Informatique Signal et Automatique de Lille, IMT Lille Douai University, Lille, France
Keyword(s):
Facial Reconstruction, 3D Morphable Model, 3D Face Imaging, Multi-Image 3D Reconstruction, Single-Image 3D Reconstruction.
Abstract:
Recently, many researchers have focused on 3D face analysis and its applications, and put a lot of work on developing its methods. Even though 3D facial images provide a better representation of the face in terms of accuracy, they are harder to acquire than 2D pictures. This is why, wide efforts have been put to develop systems which reconstruct 3D face models from 2D images. However, the 2D to 3D face reconstruction problem is still not very advanced, it is both computationally intensive and needs great space exploration to acquire accurate representations. In this paper, we present a 3D multi-image face reconstruction method built over a single image reconstruction model. We propose a novel 3D face re-construction approach based on two levels, first, the use of a single image 3d re-construction CNN model to represent vectorial embeddings and generate a 3d Face morphable model. And second, an unsupervised K-means model on top of the single image reconstruction CNN Model to optimize
its results by incorporating a multi-image reconstruction. Thanks to the introduction of a hybrid loss function, we are able to train the model without ground truth reference. Further-more, to our knowledge this is the first use of an unsupervised model alongside a weakly supervised one reaching such performance. Experiments show that our approach outperforms its counterparts in the literature both in single-image and multi-image reconstruction, and it proves that its unique and original nature are very promising to implement in other applications.
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