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Found 6 papers, 4 papers with code
ZeroEGGS: Zero-shot Example-based Gesture Generation from Speech
In a series of experiments, we first demonstrate the flexibility and generalizability of our model to new speakers and styles.
MoVi: A large multi-purpose human motion and video dataset
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.
Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules
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.
MoVi: A Large Multipurpose Motion and Video Dataset
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).
Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics.
AMASS: Archive of Motion Capture as Surface Shapes
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.
