Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …

Monocular depth estimation, which plays a crucial role in understanding 3D scene
geometry, is an ill-posed prob-lem. Recent methods have gained significant improvement by …

Single-view depth prediction is a fundamental problem in computer vision. Recently, deep
learning methods have led to significant progress, but such methods are limited by the …

We present an unsupervised learning framework for simultaneously training single-view
depth prediction and optical flow estimation models using unlabeled video sequences …

Learning based methods have shown very promising results for the task of depth estimation
in single images. However, most existing approaches treat depth prediction as a supervised …

Our ability to train end-to-end systems for 3D human pose estimation from single images is
currently constrained by the limited availability of 3D annotations for natural images. Most …

We introduce AiT, a unified output representation for various vision tasks, which is a crucial
step towards general-purpose vision task solvers. Despite the challenges posed by the high …

This paper studies single-image depth perception in the wild, ie, recovering depth from a
single image taken in unconstrained settings. We introduce a new dataset “Depth in the …