计算机科学与探索 ›› 2023, Vol. 17 ›› Issue (1): 74-87.DOI: 10.3778/j.issn.1673-9418.2205070
王仕宸,黄凯,陈志刚,张文东
出版日期:
2023-01-01
发布日期:
2023-01-01
WANG Shichen, HUANG Kai, CHEN Zhigang, ZHANG Wendong
Online:
2023-01-01
Published:
2023-01-01
摘要: 三维人体姿态估计的目的是预测出人体关节点的三维坐标位置和角度等信息,构建人体表示(如人体骨骼),以便进一步分析人体姿态。随着深度学习方法的不断推进,越来越多的基于深度学习的高性能三维人体姿态估计方法被提出。然而由于图片的人体遮挡、训练规模需求较大等原因,三维人体姿态估计仍然存在挑战。该研究目的是通过对近年来的多篇研究论文进行回顾,分析和比较这些方法的推理过程和核心要素,从不同输入的角度入手,全面阐述近年来基于深度学习的三维人体姿态估计方法。此外,还介绍了相关数据集和评价指标,在Human3.6M、Campus和Shelf数据集上对部分模型进行实验数据比对,分析对比实验结果。最后,根据本次调查的结果,讨论目前三维人体姿态估计所面临的困难和挑战,对三维人体姿态估计的未来发展进行了探讨。
王仕宸, 黄凯, 陈志刚, 张文东. 深度学习的三维人体姿态估计综述[J]. 计算机科学与探索, 2023, 17(1): 74-87.
WANG Shichen, HUANG Kai, CHEN Zhigang, ZHANG Wendong. Survey on 3D Human Pose Estimation of Deep Learning[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(1): 74-87.
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