Search Results for author: Nima Ghorbani

Found 5 papers, 4 papers with code

SOMA: Solving Optical Marker-Based MoCap Automatically

2 code implementations ICCV 2021 Nima Ghorbani, Michael J. Black

Commercial auto-labeling tools require a specific calibration procedure at capture time, which is not possible for archival data.

GRAB: A Dataset of Whole-Body Human Grasping of Objects

2 code implementations ECCV 2020 Omid Taheri, Nima Ghorbani, Michael J. Black, Dimitrios Tzionas

Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time.

Grasp Contact Prediction Grasp Generation +1

AMASS: Archive of Motion Capture as Surface Shapes

4 code implementations ICCV 2019 Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. Black

We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model; here we use SMPL [doi:10. 1145/2816795. 2818013], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh.

Cannot find the paper you are looking for? You can Submit a new open access paper.