Relevant bibliographies by topics / 3D Human Pose Estimation

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic '3D Human Pose Estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "3D Human Pose Estimation"

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Jia, Shan. "3D Human Pose Estimation: A Survey." Frontiers in Computing and Intelligent Systems 5, no. 2 (2023): 124–27. http://dx.doi.org/10.54097/fcis.v5i2.13139.

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This comprehensive review article explores the latest research advancements in the realm of estimating 3D human pose. Traditional methods such as PSM, SVM are discussed. Besides, this review also talks about deep learning-based approaches, including direct approaches, 2D-to-3D lifting and volumetric model approach for single person, top-down approaches and bottom-up approaches for multi-person pose estimation. The analysis covers the strengths and challenges of various methods, encompassing issues such as model generalization, occlusion robustness, and computational efficiency. Current researc
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Li, Jiaman, C. Karen Liu, and Jiajun Wu. "Ego-Body Pose Estimation via Ego-Head Pose Estimation." AI Matters 9, no. 2 (2023): 20–23. http://dx.doi.org/10.1145/3609468.3609473.

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Estimating 3D human motion from an ego-centric video, which records the environment viewed from the first-person perspective with a front-facing monocular camera, is critical to applications in VR/AR. However, naively learning a mapping between egocentric videos and full-body human motions is challenging for two reasons. First, modeling this complex relationship is difficult; unlike reconstruction motion from third-person videos, the human body is often out of view of an egocentric video. Second, learning this mapping requires a large-scale, diverse dataset containing paired egocentric videos
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Nguyen, Hung-Cuong, Thi-Hao Nguyen, Rafal Scherer, and Van-Hung Le. "Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications." Sensors 22, no. 14 (2022): 5419. http://dx.doi.org/10.3390/s22145419.

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Three-dimensional human pose estimation is widely applied in sports, robotics, and healthcare. In the past five years, the number of CNN-based studies for 3D human pose estimation has been numerous and has yielded impressive results. However, studies often focus only on improving the accuracy of the estimation results. In this paper, we propose a fast, unified end-to-end model for estimating 3D human pose, called YOLOv5-HR-TCM (YOLOv5-HRet-Temporal Convolution Model). Our proposed model is based on the 2D to 3D lifting approach for 3D human pose estimation while taking care of each step in the
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Wei, Guoqiang, Cuiling Lan, Wenjun Zeng, and Zhibo Chen. "View Invariant 3D Human Pose Estimation." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 12 (2020): 4601–10. http://dx.doi.org/10.1109/tcsvt.2019.2928813.

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Zhou, Lu, Yingying Chen, and Jinqiao Wang. "SlowFastFormer for 3D human pose estimation." Computer Vision and Image Understanding 243 (June 2024): 103992. http://dx.doi.org/10.1016/j.cviu.2024.103992.

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Bao, Wenxia, Zhongyu Ma, Dong Liang, Xianjun Yang, and Tao Niu. "Pose ResNet: 3D Human Pose Estimation Based on Self-Supervision." Sensors 23, no. 6 (2023): 3057. http://dx.doi.org/10.3390/s23063057.

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The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose ResNet. ResNet50 is used as the basic network for extract features. First, a convolutional block attention module (CBAM) was introduced to refine selection of significant pixels. Then, a waterfall atrous spa
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Jiang, Longkui, Yuru Wang, and Weijia Li. "Regress 3D human pose from 2D skeleton with kinematics knowledge." Electronic Research Archive 31, no. 3 (2023): 1485–97. http://dx.doi.org/10.3934/era.2023075.

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<abstract> <p>3D human pose estimation is a hot topic in the field of computer vision. It provides data support for tasks such as pose recognition, human tracking and action recognition. Therefore, it is widely applied in the fields of advanced human-computer interaction, intelligent monitoring and so on. Estimating 3D human pose from a single 2D image is an ill-posed problem and is likely to cause low prediction accuracy, due to the problems of self-occlusion and depth ambiguity. This paper developed two types of human kinematics to improve the estimation accuracy. First, taking t
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Sun, Jun, Mantao Wang, Xin Zhao, and Dejun Zhang. "Multi-View Pose Generator Based on Deep Learning for Monocular 3D Human Pose Estimation." Symmetry 12, no. 7 (2020): 1116. http://dx.doi.org/10.3390/sym12071116.

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In this paper, we study the problem of monocular 3D human pose estimation based on deep learning. Due to single view limitations, the monocular human pose estimation cannot avoid the inherent occlusion problem. The common methods use the multi-view based 3D pose estimation method to solve this problem. However, single-view images cannot be used directly in multi-view methods, which greatly limits practical applications. To address the above-mentioned issues, we propose a novel end-to-end 3D pose estimation network for monocular 3D human pose estimation. First, we propose a multi-view pose gene
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Yin, He, Chang Lv, and Yeqin Shao. "3D Human Pose Estimation Based on Transformer." Journal of Physics: Conference Series 2562, no. 1 (2023): 012067. http://dx.doi.org/10.1088/1742-6596/2562/1/012067.

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Abstract Currently, 3D human pose estimation has gradually been a well-liked subject. Although various models based on the deep neural network have produced an excellent performance, they still suffer from the ignorance of multiple feasible pose solutions and the problem of the relatively-fixed input length. To solve these issues, a coordinate transformer encoder based on a 2D pose is constructed to generate multiple feasible pose solutions, and multi-to-one pose mapping is employed to generate a reliable pose. A temporal transformer encoder is used to exploit the temporal dependencies of cons
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El Kaid, Amal, Denis Brazey, Vincent Barra, and Karim Baïna. "Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos." Sensors 22, no. 11 (2022): 4109. http://dx.doi.org/10.3390/s22114109.

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Two-dimensional (2D) multi-person pose estimation and three-dimensional (3D) root-relative pose estimation from a monocular RGB camera have made significant progress recently. Yet, real-world applications require depth estimations and the ability to determine the distances between people in a scene. Therefore, it is necessary to recover the 3D absolute poses of several people. However, this is still a challenge when using cameras from single points of view. Furthermore, the previously proposed systems typically required a significant amount of resources and memory. To overcome these restrictio
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Dissertations / Theses on the topic "3D Human Pose Estimation"

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Budaraju, Sri Datta. "Unsupervised 3D Human Pose Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291435.

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The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. The method does not use images, heatmaps, 3D pose annotations, paired/unpaired 2Dto3D skeletons, 3D priors, synthetic 2D skeletons, multiview or temporal information in any shape or form. The 2D skeleton input is taken by a VAE that encodes it in a latent space and then decodes that latent represe
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Wang, Jianquan. "A Human Kinetic Dataset and a Hybrid Model for 3D Human Pose Estimation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41437.

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Human pose estimation represents the skeleton of a person in color or depth images to improve a machine’s understanding of human movement. 3D human pose estimation uses a three-dimensional skeleton to represent the human body posture, which is more stereoscopic than a two-dimensional skeleton. Therefore, 3D human pose estimation can enable machines to play a role in physical education and health recovery, reducing labor costs and the risk of disease transmission. However, the existing datasets for 3D pose estimation do not involve fast motions that would cause optical blur for a monocular came
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Gong, Wenjuan. "3D Motion Data aided Human Action Recognition and Pose Estimation." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/116189.

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En aquest treball s’explora el reconeixement d’accions humanes i l'estimació de la seva postura en seqüències d'imatges. A diferència de les tècniques tradicionals d’aprenentatge a partir d’imatges 2D o vídeo amb la sortida anotada, en aquesta Tesi abordem aquest objectiu amb la informació de moviment 3D capturat, que ens ajudar a tancar el llaç entre les característiques 2D de la imatge i les interpretacions sobre el moviment humà.<br>En este trabajo se exploran el reconocimiento de acciones humanas y la estimación de su postura en secuencias de imágenes. A diferencia de las técnicas tradicio
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Yu, Tsz-Ho. "Classification and pose estimation of 3D shapes and human actions." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708443.

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Darby, John. "3D Human Motion Tracking and Pose Estimation using Probabilistic Activity Models." Thesis, Manchester Metropolitan University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523145.

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This thesis presents work on generative approaches to human motion tracking and pose estimation where a geometric model of the human body is used for comparison with observations. The existing generative tracking literature can be quite clearly divided between two groups. First, approaches that attempt to solve a difficult high-dimensional inference problem in the body model's full or ambient pose space, recovering freeform or unknown activity. Second, approaches that restrict inference to a low-dimensional latent embedding of the full pose space, recovering activity for which training data is
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Borodulina, A. (Anastasiia). "Application of 3D human pose estimation for motion capture and character animation." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906262670.

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Abstract. Interest in motion capture (mocap) technology is growing every day, and the number of possible applications is multiplying. But such systems are very expensive and are not affordable for personal use. Based on that, this thesis presents the framework that can produce mocap data from regular RGB video and then use it to animate a 3D character according to the movement of the person in the original video. To extract the mocap data from the input video, one of the three 3D pose estimation (PE) methods that are available within the scope of the project is used to determine where the join
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Burenius, Magnus. "Human 3D Pose Estimation in the Wild : using Geometrical Models and Pictorial Structures." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138136.

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Mehta, Dushyant [Verfasser]. "Real-time 3D human body pose estimation from monocular RGB input / Dushyant Mehta." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1220691135/34.

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Norman, Jacob. "3D POSE ESTIMATION IN THE CONTEXT OF GRIP POSITION FOR PHRI." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55166.

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For human-robot interaction with the intent to grip a human arm, it is necessary that the ideal gripping location can be identified. In this work, the gripping location is situated on the arm and thus it can be extracted using the position of the wrist and elbow joints. To achieve this human pose estimation is proposed as there exist robust methods that work both in and outside of lab environments. One such example is OpenPose which thanks to the COCO and MPII datasets has recorded impressive results in a variety of different scenarios in real-time. However, most of the images in these dataset
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Fathollahi, Ghezelghieh Mona. "Estimation of Human Poses Categories and Physical Object Properties from Motion Trajectories." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6835.

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Despite the impressive advancements in people detection and tracking, safety is still a key barrier to the deployment of autonomous vehicles in urban environments [1]. For example, in non-autonomous technology, there is an implicit communication between the people crossing the street and the driver to make sure they have communicated their intent to the driver. Therefore, it is crucial for the autonomous car to infer the future intent of the pedestrian quickly. We believe that human body orientation with respect to the camera can help the intelligent unit of the car to anticipate the future mo
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Books on the topic "3D Human Pose Estimation"

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Brauer, Jürgen. Human Pose Estimation With Implicit Shape Models. Saint Philip Street Press, 2020.

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Brauer, Juergen. Human Pose Estimation with Implicit Shape Models. Karlsruhe Scientific Publishing, 2014.

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Book chapters on the topic "3D Human Pose Estimation"

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Du, Songlin, and Takeshi Ikenaga. "Bidirectional 2D-3D Transformation for 3D Human Pose Estimation." In Human Pose Analysis. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9334-1_4.

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Du, Songlin, and Takeshi Ikenaga. "Spatial-Temporal Aggregation for 3D Human Head Pose Estimation." In Human Pose Analysis. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9334-1_8.

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Du, Songlin, and Takeshi Ikenaga. "Spatial-temporal Feature Transform for 3D Human Pose Estimation." In Human Pose Analysis. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9334-1_6.

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Du, Songlin, and Takeshi Ikenaga. "Joint Data Augmentation and Representation for 3D Human Pose Estimation." In Human Pose Analysis. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9334-1_5.

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Zhou, Zhiheng, Yue Cao, Xuanying Zhu, Henry Gardner, and Hongdong Li. "3D Human Pose Estimation with 2D Human Pose and Depthmap." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63820-7_30.

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Du, Songlin, and Takeshi Ikenaga. "Real-Time 3D Human Pose Estimation from a Single RGB Image." In Human Pose Analysis. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9334-1_7.

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He, Xuesheng, Huabin Wang, Yuan Qin, and Liang Tao. "3D Human Pose Estimation with Grouping Regression." In Image and Graphics Technologies and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9917-6_14.

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Haque, Albert, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, and Li Fei-Fei. "Towards Viewpoint Invariant 3D Human Pose Estimation." In Computer Vision – ECCV 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46448-0_10.

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Jiang, YiHeng, ZhiPeng Wang, YunLong Zhao, Yang Li, and ChunYan Liu. "Skeletal Triangulation for 3D Human Pose Estimation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78456-9_12.

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Guo, Yu, Lin Zhao, Shanshan Zhang, and Jian Yang. "Coarse-to-Fine 3D Human Pose Estimation." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_48.

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Conference papers on the topic "3D Human Pose Estimation"

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Iravani, Elham, Frederik Hasecke, Lukas Hahn, and Tobias Meisen. "Enhancing 3D Human Pose Estimation: A Novel Post-Processing Method." In 20th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013316600003912.

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Nakamura, Ren, Yutaro Hirao, Monica Perusquía-Hernández, Hideaki Uchiyama, and Kiyoshi Kiyokawa. "Generalizing Listening Human Behavior: 3D Human Pose Estimation Using Music." In 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE). IEEE, 2024. https://doi.org/10.1109/gcce62371.2024.10760628.

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Azam, Md Mushfiqur, and Kevin Desai. "A Survey on 3D Egocentric Human Pose Estimation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00171.

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Kim, Jiman. "AugData Distillation for Monocular 3D Human Pose Estimation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00763.

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Park, BoJeong, JongWoo Kim, SeoYeong Mun, Younglim Choi, and Hyunseok Kim. "GolfPoseNet: Golf-Specific 3D Human Pose Estimation Network." In 2025 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2025. https://doi.org/10.1109/iceic64972.2025.10879645.

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Hardy, Peter, and Hansung Kim. "Unsupervised Multi-Person 3D Human Pose Estimation From 2D Poses Alone." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00462.

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Chen, Ching-Hang, and Deva Ramanan. "3D Human Pose Estimation = 2D Pose Estimation + Matching." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.610.

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Roy, Soumava Kumar, Ilia Badanin, Sina Honari, and Pascal Fua. "Occlusion Resilient 3D Human Pose Estimation." In 2024 International Conference on 3D Vision (3DV). IEEE, 2024. http://dx.doi.org/10.1109/3dv62453.2024.00099.

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Wang, Min, Xipeng Chen, Wentao Liu, Chen Qian, Liang Lin, and Lizhuang Ma. "DRPose3D: Depth Ranking in 3D Human Pose Estimation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/136.

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In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation. Instead of accurate 3D positions, the depth ranking can be identified by human intuitively and learned using the deep neural network more easily by solving classification problems. Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D pose regression in two-stage methods from being ill-posed. In our method, firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to extract depth rankings of human joints from images. Secondly,
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Manesco, João Renato Ribeiro, Stefano Berretti, and Aparecido Nilceu Marana. "3D Human Pose Estimation Based on Monocular RGB Images and Domain Adaptation." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/sibgrapi.est.2024.31641.

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Human pose estimation in monocular images is a challenging problem in Computer Vision. Currently, while 2D poses find extensive applications, the use of 3D poses suffers from data scarcity due to the difficulty of acquisition. Therefore, fully convolutional approaches struggle due to limited 3D pose labels, prompting a two-step strategy leveraging 2D pose estimators, which does not generalize well to unseen poses, requiring the use of domain adaptation techniques. In this work, we introduce a novel Domain Unified Approach called DUA, which, through a unique combination of three modules on top
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Reports on the topic "3D Human Pose Estimation"

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Video-based 3D pose estimation for residential roofing (dataset). U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, 2022. http://dx.doi.org/10.26616/nioshrd-1042-2022-0.

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