Robust Non-rigid Motion Tracking and Surface Reconstruction Using L0 Regularization

Abstract

We present a new motion tracking method to robustly reconstruct non-rigid geometries and motions from single view depth inputs captured by a consumer depth sensor. The idea comes from the observation of the existence of intrinsic articulated subspace in most of non-rigid motions. To take advantage of this characteristic, we propose a novel L0 based motion regularizer with an iterative optimization solver that can implicitly constrain local deformation only on joints with articulated motions, leading to reduced solution space and physical plausible deformations. The L0 strategy is integrated into the available non-rigid motion tracking pipeline, forming the proposed L0-L2 non-rigid motion tracking method that can adaptively stop the tracking error propagation. Extensive experiments over complex human body motions with occlusions, face and hand motions demonstrate that our approach substantially improves tracking robustness and surface reconstruction accuracy.