Rethinking motion keyframe extraction: a greedy procedural approach using a neural control rig

3D animators traditionally use a "pose to pose"approach, whereas motion capture (MoCap) tools generate a pose for every frame, making the motion challenging to edit. We argue that current keyframe extraction methods are inadequate for human editing. To address this, we propose a novel approach that bridges the gap between MoCap animations and traditional 3D artist tools. Our contributions include learning a neural control rig as a differentiable proxy for more accurate key pose interpolation and formulating the task as an optimization problem, solved efficiently with a greedy dynamic programming algorithm.