3D face reconstruction is the task of reconstructing a face from an image into a 3D form (or mesh).
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3D face alignment of monocular images is a crucial process in the recognition of faces with disguise. 3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference. This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework. We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism.
The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions.
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
SOTA for Face Alignment on AFLW2000-3D
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce.
We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
#2 best model for 3D Face Reconstruction on Florence (Average 3D Error metric)
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
SOTA for Face Alignment on AFLW-LFPA
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).