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$L_1$-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.

人体骨骼提取对于形状抽象，估计和分析至关重要。但是，由于稀疏数据或噪声的存在以及点云内连接性的不足，很难实施。为了解决这个问题，我们从人体的连续点集提出了基于L _0-正则化的骨架优化方法。我们首先从多视角人类运动图像中给出密集点云的初始重构，然后提取L _1。${\mathrm{大号}}_{\mathrm{1个}}$-从每个点集分别获取-中间骨架，然后将所有骨架点划分为语义成分，然后从该语义成分中将已划分的点集采样为骨架序列。通过进一步观察反映相同身体动作的连续帧可能呈现相似的运动轨迹，我们在相邻帧之间时空建立了几何相关性。具体来说，我们的方法提出了时间约束和空间约束，其中第一个约束不仅考虑每个帧与其他帧之间的相关性，而且还考虑相邻帧之间的相关性，第二个约束则描述同一骨架内的相关性块和关节点内的不同块之间表示，以防止骨骼点的非等距分布。L ₀优化不同框架的所有点集。实验结果表明，我们的方法能够在运动过程中恢复丢失的骨架点，校正骨架中的异常值和平滑骨架，同时保留这些骨架的动作特征。