3D face reconstruction is the task of reconstructing a face from an image into a 3D form (or mesh).
( Image credit: Deep3DFaceReconstruction )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model.
#2 best model for 3D Face Reconstruction on Florence
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.
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.
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)
Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image.