1 code implementation • 15 Sep 2022 • Saeed Ghorbani, Ylva Ferstl, Daniel Holden, Nikolaus F. Troje, Marc-André Carbonneau
In a series of experiments, we first demonstrate the flexibility and generalizability of our model to new speakers and styles.
1 code implementation • PLOS ONE 2021 • Saeed Ghorbani, Kimia Mahdaviani, Anne Thaler, Konrad Kording, Douglas James Cook, Gunnar Blohm, Nikolaus F. Troje
Large high-quality datasets of human body shape and kinematics lay the foundation for modelling and simulation approaches in computer vision, computer graphics, and biomechanics.
no code implementations • 18 Oct 2020 • Alireza Sepas-Moghaddam, Saeed Ghorbani, Nikolaus F. Troje, Ali Etemad
In this context, we propose a novel deep network, learning to transfer multi-scale partial gait representations using capsules to obtain more discriminative gait features.
1 code implementation • 4 Mar 2020 • Saeed Ghorbani, Kimia Mahdaviani, Anne Thaler, Konrad Kording, Douglas James Cook, Gunnar Blohm, Nikolaus F. Troje
In five capture rounds, the same actors and movements were recorded using different hardware systems, including an optical motion capture system, video cameras, and inertial measurement units (IMU).
no code implementations • 31 Jul 2019 • Saeed Ghorbani, Ali Etemad, Nikolaus F. Troje
Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics.
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.