Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Advancements in deep learning techniques and the availability of free, large databases
have made it possible, even for non-technical people, to either manipulate or generate …

We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried
point on any physical surface throughout a video sequence. Our approach employs two …

Research in automatic analysis of facial expressions mainly focuses on recognising the
seven basic ones. However, compound expressions are more diverse and represent the …

Generic motion understanding from video involves not only tracking objects, but also
perceiving how their surfaces deform and move. This information is useful to make …

Existing methods of 3D dthus limiting the scope of their practical applications. In this paper,
we propose a novel regression framework which makes a balance among speed, accuracy …

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 …

The locations of the fiducial facial landmark points around facial components and facial
contour capture the rigid and non-rigid facial deformations due to head movements and …

Over the past few years, there has been an increased interest in automatic facial behavior
analysis and understanding. We present OpenFace-an open source tool intended for …

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 …