40 papers with code • 0 benchmarks • 5 datasets
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
Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.
To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface.
To tackle these problems, we propose 1) a low-cost facial texture acquisition method, 2) a shape transfer algorithm that can transform the shape of a 3DMM mesh to games, and 3) a new pipeline for training 3D game face reconstruction networks.
Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes.