We propose a straightforward method that simultaneously reconstructs the 3D facial
structure and provides dense alignment. To achieve this, we design a 2D representation …

This paper investigates how far a very deep neural network is from attaining close to
saturating performance on existing 2D and 3D face alignment datasets. To this end, we …

In this work we propose a novel model-based deep convolutional autoencoder that
addresses the highly challenging problem of reconstructing a 3D human face from a single …

Heatmap regression with a deep network has become one of the mainstream approaches to
localize facial landmarks. However, the loss function for heatmap regression is rarely …

In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method
based on a deep neural network architecture. DAN consists of multiple stages, where each …

We propose an end-to-end deep learning architecture for word-level visual speech
recognition. The system is a combination of spatiotemporal convolutional, residual and …

The reconstruction of dense 3D models of face geometry and appearance from a single
image is highly challenging and ill-posed. To constrain the problem, many approaches rely …

From holistic low-dimension feature-based segmentation to deep polynomial neural
networks, Face Recognition (FR) accuracy has increased dramatically since its early days …

Our goal is to design architectures that retain the groundbreaking performance of CNNs for
landmark localization and at the same time are lightweight, compact and suitable for …

We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for
3D Dense Head Alignment in-the-wild. It contains annotations of over 3.5 K landmarks that …