activity
20142023
most citedMoCap-guided Data Augmentation for 3D Pose Estimation in the Wild

197 citations · 571 across the 21 of their papers we have counts for

collaborators

5 papers

cs.CV2016197 cited

MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild

Grégory Rogez, Cordelia Schmid

This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. S…

cs.CV201542 cited

Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals

Minsu Cho, Suha Kwak, Cordelia Schmid +1

This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully un…

cs.CV201559 cited

EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow

Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui +1

We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions. It consists of two steps: i) dense matching by edge-preservi…

cs.CV201444 cited

Weakly Supervised Action Labeling in Videos Under Ordering Constraints

Piotr Bojanowski, Rémi Lajugie, Francis Bach +4

We are given a set of video clips, each one annotated with an {\em ordered} list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associat…

cs.CV2014174 cited

Convolutional Kernel Networks

Julien Mairal, Piotr Koniusz, Zaid Harchaoui +1

An important goal in visual recognition is to devise image representations that are invariant to particular transformations. In this paper, we address this goal with a new type of…