Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …

Learning useful representations with little or no supervision is a key challenge in artificial
intelligence. We provide an in-depth review of recent advances in representation learning …

We present iNeRF, a framework that performs mesh-free pose estimation by" inverting" a
Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the …

A neural radiance field (NeRF) is a scene model supporting high-quality view synthesis,
optimized per scene. In this paper, we explore enabling user editing of a category-level …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

A key technical challenge in performing 6D object pose estimation from RGB-D image is to
fully leverage the two complementary data sources. Prior works either extract information …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …

We present new intuitions and theoretical assessments of the emergence of disentangled
representation in variational autoencoders. Taking a rate-distortion theory perspective, we …

We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We propose …