Learning Inverse Rig Mappings by Nonlinear Regression

@article{Holden2016LearningIR,
  title={Learning Inverse Rig Mappings by Nonlinear Regression},
  author={Daniel Holden and Jun Saito and Taku Komura},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2016},
  volume={23},
  pages={1167-1178}
}
We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control… CONTINUE READING