3D face reconstruction is the task of reconstructing a face from an image into a 3D form (or mesh).
( Image credit: Deep3DFaceReconstruction )
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We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
SOTA for Face Alignment on AFLW-LFPA
The 3D shapes of faces are well known to be discriminative.
#4 best model for 3D Face Reconstruction on Florence (Average 3D Error metric)
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).
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)
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
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.
With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images.