Learning an Inverse Rig Mapping for Character Animation

How to read this

Category

Method: learned inverse-rig mapping for animation

Contributions

• Treats the rig as a black box and learns an offline approximation of its inverse with Gaussian Process Regression using a multiquadric kernel.
• Learns the Jacobian of the rig function so predictions can be refined by gradient descent to match target joint positions.
• Generalizes across quadruped, biped, deformable-mesh and facial rigs, enabling mocap, motion editing and full-body IK on rigged characters.

Context

A data-driven take on rig inversion and inverse kinematics, learning the mapping from sparse animator-built example poses rather than hand-deriving it.

Correctness

Quality hinges on having representative animator example poses and a learnable inverse; the GPR fit plus Jacobian refinement is what keeps predictions animator-like, so coverage of the pose space and rig nonlinearity are the things to watch.

Clarity

Accessible at a high level; a first pass conveys the inverse-rig idea, do a second pass for the GPR kernel and Jacobian-refinement formulation.

How to read it

First pass for the black-box-inverse framing and why animator-data priors matter; second pass on the regression plus gradient-descent refinement if you intend to reimplement.