105 citations · 652 across the 49 of their papers we have counts for
6 papers · 1 filter
MagicPony: Learning Articulated 3D Animals in the Wild
Shangzhe Wu, Ruining Li, Tomas Jakab +2
We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input. We pres…
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
Laurynas Karazija, Subhabrata Choudhury, Iro Laina +2
We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision.…
VTC: Improving Video-Text Retrieval with User Comments
Laura Hanu, James Thewlis, Yuki M. Asano +1
Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and cons…
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina +1
Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled…
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Subhabrata Choudhury, Laurynas Karazija, Iro Laina +2
Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos. However, compared to using appearance, it has some blind spots, such…
De-rendering 3D Objects in the Wild
Felix Wimbauer, Shangzhe Wu, Christian Rupprecht
With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are…