One of the major challenges encountered by current face recognition techniques lies in the
difficulties of handling varying poses, ie, recognition of faces in arbitrary in-depth rotations …

The human visual system is the most complex pattern recognition device known. In ways
that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous …

We propose a method to learn 3D deformable object categories from raw single-view
images, without external supervision. The method is based on an autoencoder that factors …

Lighting plays a central role in conveying the essence and depth of the subject in a portrait
photograph. Professional photographers will carefully control the lighting in their studio to …

Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a
challenging problem that has a high relevance for several human-computer interaction …

A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the
world from flat, 2D images of that world. Traditional methods for recovering scene properties …

Recent progress on photometric stereo extends the technique to deal with general materials
and unknown illumination conditions. However, due to the lack of suitable benchmark data …

We present a generative appearance-based method for recognizing human faces under
variation in lighting and viewpoint. Our method exploits the fact that the set of images of an …

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line
drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a …

This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes
based on deep learning. Unlike previous approaches that heavily rely on assumptions of …