Abstract
A complete 3D face reconstruction requires to explicitly model the eyeglasses on the face, which is less investigated in the literature. In this paper, we present an automatic system that recovers the 3D shape of eyeglasses from a single face image with an arbitrary head pose. To achieve this goal, we first trains a neural network to jointly perform glasses landmark detection and segmentation, which carry the sparse and dense glasses shape information respectively for 3D glasses pose estimation and shape recovery. To solve the ambiguity in 2D to 3D reconstruction, our system fully explores the prior knowledge including the relative motion constraint between face and glasses and the planar and symmetric shape prior feature of glasses. From the qualitative and quantitative experiments, we see that our system reconstructs promising 3D shapes of eyeglasses for various poses.
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Acknowledgements
This work was supported by the National Key R&D Program of China 2018YFA0704000, the NSFC (No. 61822111, 61727808, 61671268) and Beijing Natural Science Foundation (JQ19015, L182052).
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Wang, Y., Wang, Q., Xu, F. (2020). Eyeglasses 3D Shape Reconstruction from a Single Face Image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12370. Springer, Cham. https://doi.org/10.1007/978-3-030-58595-2_23
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