We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on Face Alignment on AFLW-LFPA
In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.
Ranked #2 on Face Alignment on AFLW
In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes.
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #3 on Face Alignment on AFLW2000-3D
To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface.
Our DECA (Detailed Expression Capture and Animation) model is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict detail, shape, albedo, expression, pose and illumination parameters from a single image.
Ranked #1 on 3D Face Reconstruction on NoW Benchmark
Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes.