254 citations · 268 across the 2 of their papers we have counts for
5 papers · 1 filter
SOMA: Solving Optical Marker-Based MoCap Automatically
Nima Ghorbani, Michael J. Black
Marker-based optical motion capture (mocap) is the "gold standard" method for acquiring accurate 3D human motion in computer vision, medicine, and graphics. The raw output of these…
hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
Ci Li, Nima Ghorbani, Sofia Broomé +5
In this paper we present our preliminary work on model-based behavioral analysis of horse motion. Our approach is based on the SMAL model, a 3D articulated statistical model of ani…
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Omid Taheri, Nima Ghorbani, Michael J. Black +1
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shap…
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani +4
To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To…
AMASS: Archive of Motion Capture as Surface Shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje +2
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…