Patches Detection and Fusion for 3D Face Cloning.
3D face clones are used in many fields such as video games and Human-Computer Interaction. However, high-resolution sensors generating high quality clones are expensive and not accessible to all. In this paper, we propose to make a fully automated and accurate 3D reconstruction of a face with a low cost RGB-D camera. For each subject, we capture the depth and RGB data of their face in different positions while performing a rotational movement of the head. We fit a 3D Morphable Face Model on each frame to eliminate noise, increase resolution and provide a structured mesh. This type of mesh is a mesh which the semantic and topological structure is known. We propose to only keep the suitable parts of each mesh called Patch. This selection is performed using an error distance and the direction of the normal vectors. To create the 3D face clone, we merge the different patches of each mesh. These patches contain relevant information on the specificity of individuals and lead to the construction of a more accurate clone. We perform quantitative tests by comparing our clone to ground truth and qualitative tests by comparing visual features. These results show that our method outperforms the FaceWarehouse process of Cao et al . This 3D face clone on a structured mesh can be used as pretreatment in applications such as emotion analysis  or facial animation.
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