nghorbani/human_body_prior

What it solves

Modeling the 3D pose of the human body is difficult because it is high-dimensional and complex. This project provides a way to define a distribution of valid human poses, allowing systems to penalize impossible body positions while accepting natural ones. This is particularly useful for Inverse Kinematics (IK) tasks, such as fitting a 3D body model to 2D images or 3D motion capture markers.

How it works

VPoser is a variational autoencoder (VAE) trained on the AMASS dataset, which contains a vast collection of human motion capture data. It learns a low-dimensional latent representation of SMPL body poses. Because it is end-to-end differentiable, it can be used as a prior to guide optimization processes, ensuring that the resulting body poses remain anatomically plausible.

Who it’s for

It is designed for researchers and developers working on 3D human body modeling, computer vision, and motion capture, specifically those using the SMPL body model family.

Highlights

• Differentiable Prior: Allows for seamless integration into optimization pipelines.
• Inverse Kinematics Engine: Includes a comprehensive IK engine that supports batch processing and flexible keypoint definitions (3D joints or 2D image points).
• Low-Dimensional Representation: Simplifies the complex high-dimensional space of human poses into a more manageable latent space.
• Joint Correlation Modeling: Effectively captures how different joints in the body move in relation to one another.