Markerless Multi-view 3D Human Pose Estimation: a survey

• URL: http://arxiv.org/abs/2407.03817v1
• Date: Thu, 4 Jul 2024 10:44:35 GMT
• Title: Markerless Multi-view 3D Human Pose Estimation: a survey
• Authors: Ana Filipa Rodrigues Nogueira, Hélder P. Oliveira, Luís F. Teixeira,
• Abstract summary: 3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. No method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost.
• Score: 0.49157446832511503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human-robot interaction, surveillance systems or sports, among many others. However, several obstacles such as occlusions, random camera perspectives, or the scarcity of 3D labelled data, have been hampering the models' performance and limiting their deployment in real-world scenarios. The higher availability of cameras has led researchers to explore multi-view solutions due to the advantage of being able to exploit different perspectives to reconstruct the pose. Thus, the goal of this survey is to present an overview of the methodologies used to estimate the 3D pose in multi-view settings, understand what were the strategies found to address the various challenges and also, identify their limitations. Based on the reviewed articles, it was possible to find that no method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Due to the existing trade-off between complexity and performance, the best method depends on the application scenario. Therefore, further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost. To this goal, techniques such as active learning, methods that learn with a low level of supervision, the incorporation of temporal consistency, view selection, estimation of depth information and multi-modal approaches might be interesting strategies to keep in mind when developing a new methodology to solve this task.

Related papers

• 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)
• Markerless 3D human pose tracking through multiple cameras and AI: Enabling high accuracy, robustness, and real-time performance [0.0]
  Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Recent advances in Artificial Intelligence have allowed for markerless solutions. We propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion.
  arXiv  Detail & Related papers  (2023-03-31T15:06:50Z)
• Towards unconstrained joint hand-object reconstruction from RGB videos [81.97694449736414]
  Reconstructing hand-object manipulations holds a great potential for robotics and learning from human demonstrations. We first propose a learning-free fitting approach for hand-object reconstruction which can seamlessly handle two-hand object interactions.
  arXiv  Detail & Related papers  (2021-08-16T12:26:34Z)
• 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)
• 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos [107.36352212367179]
  We propose RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed method is able to learn 3D body pose and shape across different resolutions with one single model. We extend the RSC-Net to handle low-resolution videos and apply it to reconstruct textured 3D pedestrians from low-resolution input.
  arXiv  Detail & Related papers  (2021-03-11T06:52:12Z)
• HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation [54.23770284299979]
  This paper introduces a novel form of supervision - Hierarchical Multi-person Ordinal Relations (HMOR) HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically. An integrated top-down model is designed to leverage these ordinal relations in the learning process. The proposed method significantly outperforms state-of-the-art methods on publicly available multi-person 3D pose datasets.
  arXiv  Detail & Related papers  (2020-08-01T07:53:27Z)
• 3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning [105.49950571267715]
  Existing deep learning methods for 3D human shape and pose estimation rely on relatively high-resolution input images. We propose RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. We show that both these new training losses provide robustness when learning 3D shape and pose in a weakly-supervised manner.
  arXiv  Detail & Related papers  (2020-07-27T16:19:52Z)
• Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild [101.70320427145388]
  We propose a weakly-supervised approach that does not require 3D annotations and learns to estimate 3D poses from unlabeled multi-view data. We evaluate our proposed approach on two large scale datasets.
  arXiv  Detail & Related papers  (2020-03-17T08:47:16Z)

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.