The articulated 3D pose of the human body is high-dimensional and complex. Many applications make use of a prior distribution over valid human poses, but modeling this distribution is difficult. Here we present VPoser, a learning based variational human pose prior trained from a large dataset of human poses represented as SMPL bodies. This body prior can be used as an Inverse Kinematics (IK) solver for many tasks such as fitting a body model to images as the main contribution of this repository for SMPLify-X. VPoser has the following features:
- defines a prior of SMPL pose parameters
- is end-to-end differentiable
- provides a way to penalize impossible poses while admitting valid ones
- effectively models correlations among the joints of the body
- introduces an efficient, low-dimensional, representation for human pose
- can be used to generate valid 3D human poses for data-dependent tasks
The code in this repository is developed by Nima Ghorbani while at Perceiving Systems, Max-Planck Institute for Intelligent Systems, Tübingen, Germany.
If you have any questions you can contact us at smplx@tuebingen.mpg.de.
For commercial licensing, contact ps-licensing@tue.mpg.de