Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection. In this paper, for the first time, we aim at providing a very dense 3D alignment for largepose face images. To achieve this, we train a CNN to estimate the 3D face shape, which not only aligns limited facial landmarks but also fits face contours and SIFT feature points. Moreover, we also address the bottleneck of training CNN with multiple datasets, due to different landmark markups on different datasets, such as 5, 34, 68. Experimental results show our method not only provides highquality, dense 3D face fitting but also outperforms the stateof-the-art facial landmark detection methods on challenging datasets. Our model can run at real time during testing and it's available at
In REF , Liu et al. use multi-constraints to train a CNN model, jointly estimating the 3DMM coefficient and provides very dense 3D alignment.
Dense Face Alignment
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Computer Science