Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Although significant progress has been made to audio-driven talking face generation,
existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In …

One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …

We present a joint audio-visual model for isolating a single speech signal from a mixture of
sounds such as other speakers and background noise. Solving this task using only audio as …

In the past few years, a lot of work has been done towards reconstructing the 3D facial
structure from single images by capitalizing on the power of Deep Convolutional Neural …

As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is
widely used in facial analysis, eg, model fitting, image synthesis. Conventional 3DMM is …

We present a method for training a regression network from image pixels to 3D morphable
model coordinates using only unlabeled photographs. The training loss is based on features …

Despite recent advances in face recognition using deep learning, severe accuracy drops are
observed for large pose variations in unconstrained environments. Learning pose-invariant …

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 …

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face
analysis tasks have accomplished astounding performance, with applications including, but …