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Leveraging Temporal Joint Depths for Improving 3D Human Pose Estimation in Video [article]

Naoki Kato, Hiroto Honda, Yusuke Uchida
2020 arXiv   pre-print
The effectiveness of the approaches to predict 3D poses from 2D poses estimated in each frame of a video has been demonstrated for 3D human pose estimation.  ...  In this paper, we propose to estimate a 3D pose in each frame of a video and refine it considering temporal information.  ...  INTRODUCTION 3D human pose estimation aims to localize human joints in a 3D coordinate system.  ... 
arXiv:2011.02172v1 fatcat:p55pko2mpjfr7dafmvly2jnqkq

U4D: Unsupervised 4D Dynamic Scene Understanding

Armin Mustafa, Chris Russell, Adrian Hilton
2019 2019 IEEE/CVF International Conference on Computer Vision (ICCV)  
We further leverage recent advances in 3D pose estimation to constrain the joint semantic instance segmentation and 4D temporally coherent reconstruction.  ...  Our approach simultaneously estimates a detailed model that includes a per-pixel semantically and temporally coherent reconstruction, together with instance-level segmentation exploiting photoconsistency  ...  We exploit advances in 3D human-pose estimation to propose the first approach for 4D (3D in time) human-pose based scene understanding of general dynamic scenes with multiple interacting dynamic objects  ... 
doi:10.1109/iccv.2019.01052 dblp:conf/iccv/Mustafa0H19 fatcat:gljhpdkiwrfrzpkg5cotp7nqva

Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective [article]

Wu Liu, Qian Bao, Yu Sun, Tao Mei
2021 arXiv   pre-print
Recently, benefited from the deep learning technologies, a significant amount of research efforts have greatly advanced the monocular human pose estimation both in 2D and 3D areas.  ...  We believe this survey will provide the readers with a deep and insightful understanding of monocular human pose estimation.  ...  Furthermore, given a video sequence, 2D pose estimation can exploit temporal information to boost keypoint prediction in a video system.  ... 
arXiv:2104.11536v1 fatcat:tdag2jq2vjdrjekwukm5nu7l6a

Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective

Wu Liu, Tao Mei
2022 ACM Computing Surveys  
Especially, we provide insightful analyses for the intrinsic connections and methods evolution from 2D to 3D pose estimation.  ...  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.  ...  Furthermore, given a video sequence, 2D pose estimation can exploit temporal information to boost keypoint prediction in a video system.  ... 
doi:10.1145/3524497 fatcat:4pbvntngrnfp7lqhcpjmy7p2fq

Exploiting temporal information for 3D pose estimation [article]

Mir Rayat Imtiaz Hossain, James J. Little
2018 arXiv   pre-print
In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses.  ...  Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses.  ...  Using temporal information Since estimating poses for each frame individually leads to incoherent and jittery predictions over a sequence, many approaches tried to exploit temporal information [42, 43  ... 
arXiv:1711.08585v3 fatcat:rgitnojfi5f4dlbbnrjmfwd3ry

Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework [article]

Mehwish Ghafoor, Arif Mahmood
2022 arXiv   pre-print
Temporal information has also been exploited to better estimate the missing joints.  ...  3D human pose estimation using monocular images is an important yet challenging task.  ...  Most of these methods get static pose as input and therefore do not use temporal information. Some later methods exploit the temporal information for 3D pose estimation. Hossain et al.  ... 
arXiv:2203.04113v1 fatcat:hob7dmba6zdspaqlmr6xegvrei

SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation [article]

John Yang, Hyung Jin Chang, Seungeui Lee, Nojun Kwak
2020 arXiv   pre-print
We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand pose estimation  ...  In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation  ...  Acknowledgement This work was supported by IITP grant funded by the Korea government (MSIT) (No.2019-0-01367, Babymind) and Next-Generation Information Computing Development Program through the NRF of  ... 
arXiv:2007.05168v1 fatcat:ksmivqhqbnenxgfdtbcybowkiq

Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation [article]

Ziniu Wan, Zhengjia Li, Maoqing Tian, Jianbo Liu, Shuai Yi, Hongsheng Li
2021 arXiv   pre-print
3D human shape and pose estimation is the essential task for human motion analysis, which is widely used in many 3D applications.  ...  It regards pose estimation as a top-down hierarchical process similar to SMPL kinematic tree.  ...  It is difficult to directly estimate the 3D human shape and pose from monocular images without any 3D information.  ... 
arXiv:2109.02303v1 fatcat:dsf4sjdvw5ajpnrpwmtxpyjb2i

Occluded Human Body Capture with Self-Supervised Spatial-Temporal Motion Prior [article]

Buzhen Huang, Yuan Shu, Jingyi Ju, Yangang Wang
2022 arXiv   pre-print
There are two main reasons: the one is that the occluded motion capture is inherently ambiguous as various 3D poses can map to the same 2D observations, which always results in an unreliable estimation  ...  To address the obstacles, our key-idea is to employ non-occluded human data to learn a joint-level spatial-temporal motion prior for occluded human with a self-supervised strategy.  ...  The dataset and code are publicly available. 2 Related Work Occluded 3D human pose estimation Although the 3D human pose estimation has progressively developed in recent years, it still cannot achieve  ... 
arXiv:2207.05375v1 fatcat:qdhdmpfyhrd3ljjkqrcialgzxq

A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video [article]

Junfa Liu, Juan Rojas, Zhijun Liang, Yihui Li, Yisheng Guan
2020 arXiv   pre-print
Spatio-temporal information is key to resolve occlusion and depth ambiguity in 3D pose estimation.  ...  , and achieves competitive performance on 2D-to-3D video pose estimation.  ...  Recent works have exploited temporal information to obtain more robust and smooth 3D poses [3] , [6] - [8] , [13] . For instance, Hossain et al.  ... 
arXiv:2003.14179v4 fatcat:5yg3uimk5jadlficc7wuvyzy6a

Recurrent 3D Pose Sequence Machines

Mude Lin, Liang Lin, Xiaodan Liang, Keze Wang, Hui Cheng
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
It is thus critical to exploit rich spatial and temporal long-range dependencies among body joints for accurate 3D pose sequence prediction.  ...  Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset show that our RPSM outperforms all state-of-the-art approaches for 3D pose estimation. * Corresponding author is Liang Lin.  ...  We propose a novel Recurrent 3D Pose Sequence Machine (RPSM) for estimating 3D human poses from a sequence of images.  ... 
doi:10.1109/cvpr.2017.588 dblp:conf/cvpr/LinLLWC17 fatcat:hxtwzipsg5ajjgxf4i72wfgtlm

Recurrent 3D Pose Sequence Machines [article]

Mude Lin and Liang Lin and Xiaodan Liang and Keze Wang and Hui Cheng
2017 arXiv   pre-print
It is thus critical to exploit rich spatial and temporal long-range dependencies among body joints for accurate 3D pose sequence prediction.  ...  Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset show that our RPSM outperforms all state-of-the-art approaches for 3D pose estimation.  ...  We propose a novel Recurrent 3D Pose Sequence Machine (RPSM) for estimating 3D human poses from a sequence of images.  ... 
arXiv:1707.09695v1 fatcat:66ce34u2jffbpjz2myd2mszfk4

Deep 3D human pose estimation: A review

Jinbao Wang, Shujie Tan, Xiantong Zhen, Shuo Xu, Feng Zheng, Zhenyu He, Ling Shao
2021 Computer Vision and Image Understanding  
Applications Since 3D pose representation provides additional depth information compared with 2D pose representation, 3D human pose estimation enables more widespread applications.  ...  Three-dimensional (3D) human pose estimation involves estimating the articulated 3D joint locations of a human body from an image or video.  ...  First, for recovering 3D human pose from a sequence of images, temporal information could be exploited to reduce the depth ambiguity.  ... 
doi:10.1016/j.cviu.2021.103225 fatcat:hvlgjuxd2zfgji6k4y4g65cs7y

Learning 3D Human Pose from Structure and Motion [chapter]

Rishabh Dabral, Anurag Mundhada, Uday Kusupati, Safeer Afaque, Abhishek Sharma, Arjun Jain
2018 Lecture Notes in Computer Science  
3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.  ...  We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations.  ...  Given independent pose estimates from SAP-Net, we seek to exploit the information from a set of adjacent pose-estimates P adj to improve the inference for the required pose P .  ... 
doi:10.1007/978-3-030-01240-3_41 fatcat:xqqmmqwhjvb73pmnr7sf6qxh2e

Self-Attentive 3D Human Pose and Shape Estimation from Videos [article]

Yun-Chun Chen, Marco Piccirilli, Robinson Piramuthu, Ming-Hsuan Yang
2021 arXiv   pre-print
We consider the task of estimating 3D human pose and shape from videos.  ...  In this work, we present a video-based learning algorithm for 3D human pose and shape estimation. The key insights of our method are two-fold.  ...  Related Work 3D human pose and shape estimation. Existing methods for 3D human pose and shape estimation can be broadly categorized as frame-based and video-based.  ... 
arXiv:2103.14182v2 fatcat:2ve7vmgkqvfl3kav4zdt5qiuv4
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