Recovering human pose from RGB images and videos has drawn increasing attention in recent years owing to minimum sensor requirements and applicability in diverse fields such as human-computer interaction,robotics,video analytics,and augmented reality.Although a large amount of work has been devoted to this field,3D human pose estimation based on monocular images or videos remains a very challenging task due to a variety of difficulties such as depth ambiguities,occlusion,background clutters,and lack of training data.In this survey,we summarize recent advances in monocular 3D human pose estimation.We provide a general taxonomy to cover existing approaches and analyze their capabilities and limitations.We also present a summary of extensively used datasets and metrics,and provide a quantitative comparison of some representative methods.Finally,we conclude with a discussion on realistic challenges and open problems for future research directions.
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机译：Experimental use of the inflection point test for force deconvolution in frequency-modulation atomic force microscopy to turn an ill-posed situation into a well-posed one by proper choice of amplitude
