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Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
[article]
2017
arXiv
pre-print
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All in all, we argue that the use of transfer learning of representations in tandem with algorithmic and data contributions is crucial for general 3D body pose estimation. ...
Using only the existing 3D pose data and 2D pose data, we show state-of-the-art performance on established benchmarks through transfer of learned features, while also generalizing to in-the-wild scenes ...
Supplemental Document: Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision This document accompanies the main paper, and the supplemental video. ...
arXiv:1611.09813v5
fatcat:uka37jvzbzefzow7td73phrfly
Deep Learning Methods for 3D Human Pose Estimation under Different Supervision Paradigms: A Survey
2021
Electronics
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The literature is reviewed, along with the general pipeline of 3D human pose estimation, which consists of human body modeling, learning-based pose estimation, and regularization for refinement. ...
Based on this literature survey, it can be concluded that each branch of 3D human pose estimation starts with fully-supervised methods, and there is still much room for multi-person pose estimation based ...
[21] is a recently published a survey of deep learning-based monocular human pose estimation, which contains both 2D and 3D human pose estimation. ...
doi:10.3390/electronics10182267
fatcat:ajnizu776ncpto3jvyh3zye2si
Leaving Flatland: Advances in 3D behavioral measurement
[article]
2021
arXiv
pre-print
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Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more ...
Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. ...
Transfer learning refers to when a network pretrained on one dataset for a specific task, for instance human 3D pose estimation, performs learns better on a different task, such as rat 3D pose estimation ...
arXiv:2112.01987v1
fatcat:z5dqj47s3fbt3d4t75bnsp2l6q
Monocular Depth Estimation using Transfer learning-An Overview
2021
E3S Web of Conferences
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Finally, the use of transfer learning approaches to monocular depth estimation is illustrated. ...
A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. ...
Monocular Depth Estimation Transfer Learning Techniques
Convolutional Neural Networks (CNNs) Researchers constructed CNN-dependent estimation networks of monocular depth that learn depth information ...
doi:10.1051/e3sconf/202130901069
fatcat:kn7nypnfnzgk7hmadubgkttmxe
Monocular Human Shape and Pose with Dense Mesh-borne Local Image Features
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2111.05319v1 fatcat:ywa3dmfh7rhv7dga3ztqnpjogyOther Versions
We propose to improve on graph convolution based approaches for human shape and pose estimation from monocular input, using pixel-aligned local image features. ...
Given a single input color image, existing graph convolutional network (GCN) based techniques for human shape and pose estimation use a single convolutional neural network (CNN) generated global image ...
Learning-based methods 1) Model-based: Model-based methods make use of a parametric 3D human body model to perform 3D human pose and shape estimation. ...
arXiv:2111.05319v3
fatcat:q3o3w3cxtfglnbyb7tvfpe62xi
Monocular Depth Estimation Based On Deep Learning: An Overview
[article]
2020
arXiv
pre-print
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Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. ...
Inferring depth information from a single image (monocular depth estimation) is an ill-posed problem. ...
MONOCULAR DEPTH ESTIMATION BASED ON DEEP
LEARNING Since humans can use priori information of the world, it is capable for them to perceive the depth information from a single image. ...
arXiv:2003.06620v1
fatcat:l5ei3ognova6xkyppflef5nqsq
Outdoor Monocular Depth Estimation: A Research Review
[article]
2022
arXiv
pre-print
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the current time is on a single source, or monocular, depth estimation. ...
While the traditional methods of estimating depth are based on depth cues and require specific equipment such as stereo cameras and configuring input according to the approach being used, the focus at ...
Supervised Learning Supervised learning networks for monocular depth estimation are trained using the Ground Truth depth maps. ...
arXiv:2205.01399v1
fatcat:mwitpnwlz5cl7ahv4wd4up5nxa
VNect
2017
ACM Transactions on Graphics
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2017 pre-print arXiv:1705.01583v1 fatcat:nkjon22onbgfpmswhtxg6hrkw4
Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. ...
We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. ...
Discriminative 2D human pose estimation is often an intermediate step to monocular 3D pose estimation. ...
doi:10.1145/3072959.3073596
fatcat:kviukzjktjbbrfjxcr2kgtytse
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
[article]
2020
arXiv
pre-print
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Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task ...
Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. ...
tracking and human pose estimation (used for pedestrian movement analysis). ...
arXiv:2006.06091v3
fatcat:nhdgivmtrzcarp463xzqvnxlwq
Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective
2022
ACM Computing Surveys
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Recently, benefiting from the deep learning technologies, a significant amount of research efforts have advanced the monocular human pose estimation both in 2D and 3D areas. ...
Estimation of the human pose from a monocular camera has been an emerging research topic in the computer vision community with many applications. ...
This paper provides a comprehensive survey on deep learning-based monocular human pose estimation for both 2D and 3D tasks. ...
doi:10.1145/3524497
fatcat:4pbvntngrnfp7lqhcpjmy7p2fq
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
[article]
2021
arXiv
pre-print
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depth-pose learning methods under a variety of challenging scenarios, and achieves state-of-the-art results among self-supervised learning-based methods on KITTI Odometry and NYUv2 dataset. ...
Extensive experiments show that our system not only reaches state-of-the-art performance on KITTI depth and flow estimation, but also significantly improves the generalization ability of existing self-supervised ...
Acknowledgements This work was partially supported by NSFC (61725204, 61521002), BNRist and MOE-Key Laboratory of Pervasive Computing. ...
arXiv:2004.01314v2
fatcat:ria3yrzwafcdxoux2y2ctmguke
Deep 3D human pose estimation: A review
2021
Computer Vision and Image Understanding
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A robot can better serve and help users if it can understand 3D poses, actions and J o u r n a l P r e -p r o o f Journal Pre-proof Abstract 3D human pose estimation from a monocular image or 2D joints ...
We argue that 3D human pose estimation from a monocular input is an inverse problem where multiple feasible solutions can exist. ...
The 3D human pose estimation from a single monocular image suffers from inherent ambiguity, and thus more supervision signals are required. ...
doi:10.1016/j.cviu.2021.103225
fatcat:hvlgjuxd2zfgji6k4y4g65cs7y
Constant Velocity Constraints for Self-Supervised Monocular Depth Estimation
2020
European Conference on Visual Media Production
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ABSTRACT We present a new method for self-supervised monocular depth estimation. ...
Contemporary monocular depth estimation methods use a triplet of consecutive video frames to estimate the central depth image. ...
[Zhou et al. 2017] proposed the first self-supervised monocular depth learning that simultaneously learns both depth and camera pose estimators during training. ...
doi:10.1145/3429341.3429355
fatcat:gx6bcnssujbx5obuyktz2b2yyu
Deep Learning-Based Human Pose Estimation: A Survey
[article]
2022
arXiv
pre-print
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Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. ...
The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions ...
[298] proposed a weakly supervised transfer learning method that uses 2D annotations of in-the-wild images as weak labels. 3D pose estimation module was connected with intermediate layers of the 2D ...
arXiv:2012.13392v4
fatcat:ypnqtq3sbncr5fuujif2dhqwji
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. ...
It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. ...
Such failure cases are common to many monocular 3D pose estimation approaches.
Conclusion We proposed a new deep learning architecture for 3D human pose estimation from monocular color images. ...
doi:10.1109/cvpr.2019.01116
dblp:conf/cvpr/HabibieXMPT19
fatcat:5h5n5puhlvfz7p52duegjtvaz4
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