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.

hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition

no code implementations • 18 Jun 2021 • Ci Li, Nima Ghorbani, Sofia Broomé, Maheen Rashid, Michael J. Black, Elin Hernlund, Hedvig Kjellström, Silvia Zuffi

In this paper we present our preliminary work on model-based behavioral analysis of horse motion.

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

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

1 code implementation • CVPR 2019 • Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, Michael J. Black

We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild.

3D Human Pose Estimation 3D Human Reconstruction +2

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.