Automatic identity recognition from ear images represents an active field of research within
the biometric community. The ability to capture ear images from a distance and in a covert …

Machine learning has long since become a keystone technology, accelerating science and
applications in a broad range of domains. Consequently, the notion of applying learning …

Unsupervised learning with generative models has the potential of discovering rich
representations of 3D scenes. While geometric deep learning has explored 3D-structure …

Abstract We present Neural Articulated Radiance Field (NARF), a novel deformable 3D
representation for articulated objects learned from images. While recent advances in 3D …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Abstract We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most …

To truly understand the visual world our models should be able not only to recognize images
but also generate them. To this end, there has been exciting recent progress on gen-erating …

Scene representation—the process of converting visual sensory data into concise
descriptions—is a requirement for intelligent behavior. Recent work has shown that neural …

We describe the first method to automatically estimate the 3D pose of the human body as
well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and …

Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …