Results 21 to 30 of about 60,919 (294)
PosturePose: Optimized Posture Analysis for Semi-Supervised Monocular 3D Human Pose Estimation
Sensors, 2023 One motivation for studying semi-supervised techniques for human pose estimation is to compensate for the lack of variety in curated 3D human pose datasets by combining labeled 3D pose data with readily available unlabeled video data—effectively ...
Lawrence Amadi, Gady Agam
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On the Calibration of Human Pose Estimation
CoRR, 2023 Most 2D human pose estimation frameworks estimate keypoint confidence in an ad-hoc manner, using heuristics such as the maximum value of heatmaps. The confidence is part of the evaluation scheme, e.g., AP for the MSCOCO dataset, yet has been largely overlooked in the development of state-of-the-art methods.
Kerui Gu, Rongyu Chen, Angela Yao
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Monocular Human Depth Estimation Via Pose Estimation
IEEE Access, 2021 We propose a novel monocular depth estimator, which improves the prediction accuracy on human regions by utilizing pose information. The proposed algorithm consists of two networks — PoseNet and DepthNet — to estimate keypoint heatmaps and ...
Jinyoung Jun +3 more
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Research Progress of Two-Dimensional Human Pose Estimation Based on Deep Learning [PDF]
Jisuanji gongcheng, 2021 The two-dimensional Human Pose Estimation(HPE) methods based on deep learning have attracted much attention for their application potential.The methods work by constructing a specific neural network architecture,and processing the extracted feature ...
LIU Yong, LI Jie, ZHANG Jianlin, XU Zhiyong, WEI Yuxing
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Recurrent Human Pose Estimation [PDF]
2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017 We propose a novel ConvNet model for predicting 2D human body poses in an image. The model regresses a heatmap representation for each body keypoint, and is able to learn and represent both the part appearances and the context of the part configuration.
Vasileios Belagiannis, Andrew Zisserman
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A Review on Human Pose Estimation
CoRR, 2021 The phenomenon of Human Pose Estimation (HPE) is a problem that has been explored over the years, particularly in computer vision. But what exactly is it? To answer this, the concept of a pose must first be understood. Pose can be defined as the arrangement of human joints in a specific manner.
Rohit Josyula, Sarah Ostadabbas
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Fast Human Pose Estimation [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Existing human pose estimation approaches often only consider how to improve the model generalisation performance, but putting aside the significant efficiency problem. This leads to the development of heavy models with poor scalability and cost-effectiveness in practical use. In this work, we investigate the under-studied but practically critical pose
Feng Zhang 0052 +2 more
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Human Modelling and Pose Estimation Overview
CoRRHuman modelling and pose estimation stands at the crossroads of Computer Vision, Computer Graphics, and Machine Learning. This paper presents a thorough investigation of this interdisciplinary field, examining various algorithms, methodologies, and practical applications.
Knap, Pawel
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2D Human pose estimation: a survey
Multimedia Systems, 2022 Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e.g., images, videos, or signals). It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields.
Haoming Chen +5 more
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Location-Free Human Pose Estimation
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained annotations for human body. To alleviate this issue, we revisit HPE and propose a location-free framework without supervision of keypoint locations. We reformulate the regression-
Xixia Xu +4 more
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