Monocular 3D facial shape reconstruction from a single 2D image with coupled-dictionary learning and sparse coding
作者：
Highlights：
• Proposing a dictionary-based parametric model of 3D facial shape that achieves more than 50 and 20% improvement in reconstruction accuracy for seen and unseen data, respectively, over the widely applied PCA-based subspace model.
• Developing an efficient algorithm for estimating the sparse 3D facial shape from 2D facial landmarks that is generalizable to different types of data, including facial images, portraits, and facial sketches.
• Developing an algorithm for 3D super-resolution that reconstructs the dense 3D face based on the estimated sparse 3D facial shape and achieves an average of 10% reduction in reconstruction error over four state-of-the-art algorithms.
摘要
•Proposing a dictionary-based parametric model of 3D facial shape that achieves more than 50 and 20% improvement in reconstruction accuracy for seen and unseen data, respectively, over the widely applied PCA-based subspace model.•Developing an efficient algorithm for estimating the sparse 3D facial shape from 2D facial landmarks that is generalizable to different types of data, including facial images, portraits, and facial sketches.•Developing an algorithm for 3D super-resolution that reconstructs the dense 3D face based on the estimated sparse 3D facial shape and achieves an average of 10% reduction in reconstruction error over four state-of-the-art algorithms.
论文关键词：Dictionary learning,Sparse coding,3D super-resolution,3D face modeling,3D face reconstruction
论文评审过程：Received 31 May 2017, Revised 26 February 2018, Accepted 4 March 2018, Available online 6 March 2018, Version of Record 16 May 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.03.002
