3-D motion recovery via low rank matrix analysis

Skeleton tracking is a useful and popular application of Kinect. However, it cannot provide accurate reconstructions for complex motions, especially in the presence of occlusion. This paper proposes a new 3-D motion recovery method based on lowrank matrix analysis to correct invalid or corrupted motions. We address this problem by representing a motion sequence as a matrix, and introducing a convex low-rank matrix recovery model, which fixes erroneous entries and finds the correct low-rank matrix by minimizing nuclear norm and `1-norm of constituent clean motion and error matrices. Experimental results show that our method recovers the corrupted skeleton joints, achieving accurate and smooth reconstructions even for complicated motions

Spatio-temporal reconstruction for 3D motion recovery

—This paper addresses the challenge of 3D motion recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. We propose a new 3D motion recovery method using spatio-temporal reconstruction, which uses joint low-rank and sparse priors to exploit temporal correlation and an isometric constraint for spatial correlation. The proposed model is formulated as a constrained optimization problem, which is efficiently solved by the augmented Lagrangian method with a Gauss-Newton solver for the subproblem of isometric optimization. Experimental results on the CMU motion capture dataset, Edinburgh dataset and two Kinect datasets demonstrate that the proposed approach achieves better motion recovery than state-of-the-art methods. The proposed method is applicable to Kinect-like skeleton tracking devices and pose estimation methods that cannot provide accurate estimation of complex motions, especially in the presence of occlusion

HUMAN4D: A human-centric multimodal dataset for motions and immersive media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing vari

Cloud point labelling in optical motion capture systems

109 p.This Thesis deals with the task of point labeling involved in the overall workflow of Optical Motion Capture Systems. Human motion capture by optical sensors produces at each frame snapshots of the motion as a cloud of points that need to be labeled in order to carry out ensuing motion analysis. The problem of labeling is tackled as a classification problem, using machine learning techniques as AdaBoost or Genetic Search to train a set of weak classifiers, gathered in turn in an ensemble of partial solvers. The result is used to feed an online algorithm able to provide a marker labeling at a target detection accuracy at a reduced computational cost. On the other hand, in contrast to other approaches the use of misleading temporal correlations has been discarded, strengthening the process against failure due to occasional labeling errors. The effectiveness of the approach is demonstrated on a real dataset obtained from the measurement of gait motion of persons, for which the ground truth labeling has been verified manually. In addition to the above, a broad sight regarding the field of Motion Capture and its optical branch is provided to the reader: description, composition, state of the art and related work. Shall it serve as suitable framework to highlight the importance and ease the understanding of the point labeling