Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods

• URL: http://arxiv.org/abs/2006.01423v1
• Date: Tue, 2 Jun 2020 07:07:45 GMT
• Title: Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
• Authors: Yucheng Chen, Yingli Tian and Mingyi He
• Abstract summary: Vision-based monocular human pose estimation is one of the most fundamental and challenging problems in computer vision. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation.
• Score: 25.3614052943568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
  We discuss the recent advances in deep learning-based object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T14:44:22Z)
• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Deep learning for 3D human pose estimation and mesh recovery: A survey [6.535833206786788]
  We present a review of recent progress over the past five years in deep learning methods for 3D human pose estimation. To the best of our knowledge, this survey is arguably the first to comprehensively cover deep learning methods for 3D human pose estimation.
  arXiv  Detail & Related papers  (2024-02-29T04:30:39Z)
• 2D Human Pose Estimation: A Survey [16.56050212383859]
  Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data. Deep learning techniques allow learning feature representations directly from the data. In this paper, we reap the recent achievements of 2D human pose estimation methods and present a comprehensive survey.
  arXiv  Detail & Related papers  (2022-04-15T08:09:43Z)
• Recovering 3D Human Mesh from Monocular Images: A Survey [49.00136388529404]
  Estimating human pose and shape from monocular images is a long-standing problem in computer vision. This survey focuses on the task of monocular 3D human mesh recovery.
  arXiv  Detail & Related papers  (2022-03-03T18:56:08Z)
• A survey of top-down approaches for human pose estimation [0.0]
  State-of-the-art methods implemented with Deep Learning have brought remarkable results in the field of human pose estimation. This paper aims to provide newcomers with an extensive review of deep learning methods-based 2D images for recognizing the pose of people.
  arXiv  Detail & Related papers  (2022-02-05T23:27:46Z)
• Single Person Pose Estimation: A Survey [45.144269986277365]
  Human pose estimation in unconstrained images and videos is a fundamental computer vision task. We summarize representative human pose methods in a structured taxonomy, with a particular focus on deep learning models and single-person image setting. We examine and survey all the components of a typical human pose estimation pipeline, including data augmentation, model architecture and backbone.
  arXiv  Detail & Related papers  (2021-09-21T09:53:15Z)
• Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective [69.44384540002358]
  We provide a comprehensive and holistic 2D-to-3D perspective to tackle this problem. We categorize the mainstream and milestone approaches since the year 2014 under unified frameworks. We also summarize the pose representation styles, benchmarks, evaluation metrics, and the quantitative performance of popular approaches.
  arXiv  Detail & Related papers  (2021-04-23T11:07:07Z)
• Deep Learning-Based Human Pose Estimation: A Survey [66.01917727294163]
  Human pose estimation has drawn increasing attention during the past decade. It has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Recent deep learning-based solutions have achieved high performance in human pose estimation.
  arXiv  Detail & Related papers  (2020-12-24T18:49:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.