Mocap Denoising via Sparsity Optimization on High‐Order Motion Feature

Abstract

Conventional human motion denoising methods often prioritize smoothness at the cost of dynamic fidelity. This aggressive smoothing tends to obliterate sharp transients, thereby diminishing the perceived forcefulness (or ‘Sense of Force') of the motion—a critical attribute for realistic animation. Inspired by physiological findings on RFD, we introduce a novel motion prior based on the sparsity of high‐order temporal derivatives. Specifically, we formulate denoising as an optimization problem that encourages jerk sparsity, serving as a kinematic proxy for impulsive force generation. Our solver achieves linear complexity, enabling efficient processing of long sequences. Extensive experiments on synthetic and real MoCap data demonstrate that our method effectively eliminates noise while preserving high‐frequency acceleration details more effectively compared to state‐of‐the‐art filtering and learning‐based approaches. User studies with professional animators confirm that our results are significantly preferred for their preserved dynamic impact. Furthermore, we showcase the versatility of our approach in applications such as forcefulness restoration and controllable editing. Our code will be made publicly available at https://github.com/ChambinLee/sparse‐jerk‐mocap‐denoising .