Naureen Mahmood — accepted AI-conference papers

2019

AMASS: Archive of Motion Capture As Surface Shapes

Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. Black

ICCV 2019poster

Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many different datasets available, they…

Cited by 1579 PDF ↗code ↗Scholar ↗

2017

Learning From Synthetic Humans

Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev +1

CVPR 2017poster

Estimating human pose, shape, and motion from images and video are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire…

Cited by 1234 PDF ↗Scholar ↗