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This paper proposes an approach to learn generic multi-modal mesh surfacerepresentations using a novel scheme for fusing texture and geometric data. Ourapproach defines an inverse mapping between different geometric descriptorscomputed on the mesh surface or its down-sampled version, and the corresponding2D texture image of the mesh, allowing the construction of fused geometricallyaugmented images (FGAI). This new fused modality enables us to learn featurerepresentations from 3D data in a highly efficient manner by simply employingstandard convolutional neural networks in a transfer-learning mode. In contrastto existing methods, the proposed approach is both computationally and memoryefficient, preserves intrinsic geometric information and learns highlydiscriminative feature representation by effectively fusing shape and textureinformation at data level. The efficacy of our approach is demonstrated for thetasks of facial action unit detection and expression classification. Theextensive experiments conducted on the Bosphorus and BU-4DFE datasets, showthat our method produces a significant boost in the performance when comparedto state-of-the-art solutions

Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection