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　L-0-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　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　L-0　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.