论文标题
单眼人类姿势估计：基于深度学习方法的调查
Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
论文作者
论文摘要
基于视觉的单眼人类姿势估计是计算机视觉中最根本，最具挑战性的问题之一，旨在从输入图像或视频序列中获得人体的姿势。深度学习技术的最新发展已在人类姿势估计领域取得了重大进步和显着的突破。这项调查广泛回顾了自2014年以来发布的最新基于深度学习的2D和3D人姿势估计方法。本文总结了挑战，主要框架，基准数据集，评估指标，绩效比较，并讨论了一些有希望的未来研究方向。
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation. This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014. This paper summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.
