We introduce a set of image transformations that can be used as corruptions to evaluate the
robustness of models as well as data augmentation mechanisms for training neural …

Neural networks have shown great abilities in estimating depth from a single image.
However, the inferred depth maps are well below one-megapixel resolution and often lack …

Image matting refers to extracting precise alpha matte from natural images, and it plays a
critical role in various downstream applications, such as image editing. Despite being an ill …

Single image depth prediction is a challenging task due to its ill-posed nature and
challenges with capturing ground truth for supervision. Large-scale disparity data generated …

The Ken Burns effect allows animating still images with a virtual camera scan and zoom.
Adding parallax, which results in the 3D Ken Burns effect, enables significantly more …

Monocular depth estimation is an ill-posed problem, and as such critically relies on scene
priors and semantics. Due to its complexity, we propose a deep neural network model based …

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed
solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge …

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer
with a classical physically motivated renderer. Given a single image and a potentially …

We present a fully data-driven method to compute depth from diverse monocular video
sequences that contain large amounts of non-rigid objects, eg, people. In order to learn …

This paper proposes a hierarchical loss for monocular depth estimation, which measures the
differences between the prediction and ground truth in hierarchical embedding spaces of …