3D Face Reconstruction
50 papers with code • 7 benchmarks • 8 datasets
3D face reconstruction is the task of reconstructing a face from an image into a 3D form (or mesh).
( Image credit: 3DDFA_V2 )
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Though tremendous strides have been made in uncontrolled face detection, accurate and efficient 2D face alignment and 3D face reconstruction in-the-wild remain an open challenge.
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database.
Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.
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.