L0-regularization-based skeleton optimization from consecutive point sets of kinetic human body

Human skeleton extraction is essential for shape abstraction, estimation and analysis. However, it is difficult to implement with the existence of sparse data or noise and the shortage of connectivity within point clouds. To tackle this problem, we propose L0-regularization-based skeleton optimization method from consecutive point sets of kinetic human body. We firstly give an initial reconstruction of a dense point cloud from multi-view human motion images, and extract L1-medial skeleton from each point set individually, and then partition all skeleton points into semantic components, from which the partitioned point set is then sampled into skeleton sequence. By further observing that consecutive frames reflecting same body actions may present similar moving trajectories, we build geometric correlations spatiotemporally between adjacent frames. To be specific, our method proposes a temporal constraint and a spatial constraint, where the first constraint considers not only the correlations between each frame and the others, but also the correlations between adjacent frames, and the second one depicts the correlation within the same skeleton block and within the joint points that between different blocks to prevent the non-equidistant distribution of the skeleton points. By integrating the above spatio-temporal constraints, we establish a sparse optimization model and apply L0 optimization to all point sets of different frames. Experimental results show that our method can recover missing skeleton points, correct outliers in skeletons and smooth skeletons in the process of movement while retaining the action features of these skeletons.