Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Abstract
We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.
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For a stream of 100 simple dynamics images from the JAAD data set, frame differencing reduces the median image size by up to 40%.
“For a stream of 100 simple dynamics images from the JAAD data set, this knob reduces the median image size by up to 40%.”
From Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge (2021) · cites this paper as [19]
For video frames with complex scene dynamics, the ratio of FIVE Lat to ONE Lat is 5.6x for the JAAD dataset and 8.4x for the DukeMTMC dataset.
“We note that for video frames with complex scene dynamics, the ratio FIVE Lat /ONE Lat is 5.6x for the JAAD dataset, and 8.4x for the DukeMTMC dataset.”
From Mez: An Adaptive Messaging System for Latency-Sensitive Multi-Camera Machine Vision at the IoT Edge (2021) · cites this paper as [19]
Top-down human pose estimation methods still dominate the leaderboard of public benchmark datasets like MS COCO, but they are usually slower than bottom-up methods.
“Although top-down methods still dominate the leaderboard of public benchmark datasets like MS COCO 10 , they are usually slower than bottom-up methods [112].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [112]
Cites
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“Insafutdinov et al. [15] built on [27] with a stronger part detectors based on ResNet [13] and image-dependent pairwise scores, and vastly improved the runtime with an incremental optimization approach, but the method still takes several minutes per image, with a limit of at most 150 part proposals.”
BackgroundMethodVery Deep Convolutional Networks for Large-Scale Image Recognition
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MethodMicrosoft COCO: Common Objects in Context
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“To obtain reliable local observations of body parts, Convolu-tional Neural Networks (CNNs) have been widely used, and have significantly boosted the accuracy on body pose estimation [23, 38, 24, 34, 35, 7, 36, 6].”
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DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.214 · Cited by 3,288 · cited 1× · see citing works
“To obtain reliable local observations of body parts, Convolu-tional Neural Networks (CNNs) have been widely used, and have significantly boosted the accuracy on body pose estimation [23, 38, 24, 34, 35, 7, 36, 6].”
MethodWei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.511 · Cited by 2,966 · cited 7× · see citing works
“…be capable of iteratively refining confidence maps by learning implicit spatial relationship between different body parts in the single person case [38]; we show the that sequential prediction architectures can also iteratively refine PAFs while capturing spatial relationship across different…”
BackgroundMethodInfluentialSelf-citation2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.471 · Cited by 2,699 · cited 1× · see citing works
“We evaluate our method on two benchmarks for multi-person pose estimation: (1) the MPII human multi-person dataset [2] and (2) the MSCOCO 2016 keypoints challenge dataset [21].”
MethodPictorial Structures for Object Recognition
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042934.15159.49 · Cited by 2,167 · cited 1× · see citing works
“The spatial model for articulated pose is either based on tree-structured graphical models [10, 30, 4, 3, 26, 40, 17], which parametrically encode the spatial relationship between adjacent parts following a kinematic chain, or non-tree models [37, 31, 20, 18, 8] that augment the tree structure with…”
MethodEfficient object localization using Convolutional Networks
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298664 · Cited by 1,365 · cited 1× · see citing works
“These top-down approaches directly leverage existing techniques for single-person pose estimation [17, 31, 18, 28, 29, 7, 30, 5, 6, 20], but suffer from early commitment: if the person detector fails–as it is prone to do when people are in close proximity–there is no recourse to recovery.”
BackgroundJoint Training of a Convolutional Network and a Graphical Model for Human Pose EstimationPreprint
Tompson, J., Jain, A., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2984 · Cited by 1,317 · cited 2× · see citing works
“Tompson et al. [35] used a deep architecture with a graphical model whose parameters are learned jointly with the network.”
BackgroundMethodDeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., & Schiele, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_3 · Cited by 1,191 · cited 12× · see citing works
“We build a two-branch network upon CPMs [38] to iteratively refine both confidence maps and PAFs with global image and spatial contexts.”
BackgroundMethodInfluentialDeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.533 · Cited by 1,090 · cited 8× · see citing works
“Table 1 compares mAP performance between our method and other approaches on the same subset of 288 testing images as in [27], and the entire MPI testing set.”
BackgroundMethodInfluentialTowards Accurate Multi-person Pose Estimation in the Wild
Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., & Murphy, K. P. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.395 · Cited by 901 · see citing works
Articulated Human Detection with Flexible Mixtures of Parts
Yang, Y., & Ramanan, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.261 · Cited by 843 · cited 1× · see citing works
“Human 2D pose estimation—the problem of localizing anatomical keypoints or “parts”—has largely focused on finding body parts of individuals [8, 4, 3, 21, 33, 13, 25, 31, 6, 24].”
BackgroundClustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Johnson, S., & Everingham, M. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.12 · Cited by 826 · cited 1× · see citing works
“The spatial model for articulated pose is either based on tree-structured graphical models [10, 30, 4, 3, 26, 40, 17], which parametrically encode the spatial relationship between adjacent parts following a kinematic chain, or non-tree models [37, 31, 20, 18, 8] that augment the tree structure with…”
MethodPictorial structures revisited: People detection and articulated pose estimation
Andriluka, M., Roth, S., & Schiele, B. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206754 · Cited by 814 · cited 1× · see citing works
“The spatial model for articulated pose is either based on tree-structured graphical models [10, 30, 4, 3, 26, 40, 17], which parametrically encode the spatial relationship between adjacent parts following a kinematic chain, or non-tree models [37, 31, 20, 18, 8] that augment the tree structure with…”
MethodFlowing ConvNets for Human Pose Estimation in Videos
Pfister, T., Charles, J., & Zisserman, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.222 · Cited by 530 · cited 1× · see citing works
“These top-down approaches directly leverage existing techniques for single-person pose estimation [17, 31, 18, 28, 29, 7, 30, 5, 6, 20], but suffer from early commitment: if the person detector fails–as it is prone to do when people are in close proximity–there is no recourse to recovery.”
BackgroundHuman Pose Estimation via Convolutional Part Heatmap Regression
Bulat, A., & Tzimiropoulos, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_44 · Cited by 526 · see citing works
Monocular 3D pose estimation and tracking by detection
Andriluka, M., Roth, S., & Schiele, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540156 · Cited by 507 · cited 1× · see citing works
“The spatial model for articulated pose is either based on tree-structured graphical models [10, 30, 4, 3, 26, 40, 17], which parametrically encode the spatial relationship between adjacent parts following a kinematic chain, or non-tree models [37, 31, 20, 18, 8] that augment the tree structure with…”
MethodPoselet Conditioned Pictorial Structures
Pishchulin, L., Andriluka, M., Gehler, P., & Schiele, B. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.82 · Cited by 375 · cited 1× · see citing works
“Human 2D pose estimation—the problem of localizing anatomical keypoints or “parts”—has largely focused on finding body parts of individuals [8, 4, 3, 21, 33, 13, 25, 31, 6, 24].”
BackgroundArticulated Pose Estimation by a Graphical Model with Image Dependent Pairwise RelationsPreprint
Chen, X., & Yuille, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.3399 · Cited by 352 · cited 1× · see citing works
“To obtain reliable local observations of body parts, Convolu-tional Neural Networks (CNNs) have been widely used, and have significantly boosted the accuracy on body pose estimation [23, 38, 24, 34, 35, 7, 36, 6].”
MethodRecurrent Human Pose Estimation
Belagiannis, V., & Zisserman, A. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.64 · Cited by 325 · cited 1× · see citing works
“These top-down approaches directly leverage existing techniques for single-person pose estimation [17, 31, 18, 28, 29, 7, 30, 5, 6, 20], but suffer from early commitment: if the person detector fails–as it is prone to do when people are in close proximity–there is no recourse to recovery.”
BackgroundStrike a Pose: Tracking People by Finding Stylized Poses
Ramanan, D., Forsyth, D. A., & Zisserman, A. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.335 · Cited by 304 · cited 1× · see citing works
“Human 2D pose estimation—the problem of localizing anatomical keypoints or “parts”—has largely focused on finding body parts of individuals [8, 4, 3, 21, 33, 13, 25, 31, 6, 24].”
BackgroundMulti-source Deep Learning for Human Pose Estimation
Ouyang, W., Chu, X., & Wang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.299 · Cited by 269 · cited 1× · see citing works
“To obtain reliable local observations of body parts, Convolu-tional Neural Networks (CNNs) have been widely used, and have significantly boosted the accuracy on body pose estimation [23, 38, 24, 34, 35, 7, 36, 6].”
MethodPose Machines: Articulated Pose Estimation via Inference Machines
Ramakrishna, V., Munoz, D., Hebert, M., Bagnell, J. A., & Sheikh, Y. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_3 · Cited by 246 · cited 1× · see citing works
“The convolutional pose machines architecture proposed by Wei et al. [38] used a multi-stage architecture based on a sequential prediction framework [29]; iteratively incorporating global context to refine part confi-dence maps and preserving multimodal uncertainty from previous iterations.”
MethodSelf-citationArticulated people detection and pose estimation: Reshaping the future
Pishchulin, L., Jain, A., Andriluka, M., Thormählen, T., & Schiele, B. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248052 · Cited by 228 · cited 2× · see citing works
“A common approach [28, 11, 33, 16] is to divide and conquer the problem by employing a person detector and performing single-person pose estimation for each detection.”
BackgroundMethodArticulated part-based model for joint object detection and pose estimation
Sun, M., & Savarese, S. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126309 · Cited by 196 · cited 2× · see citing works
“A common approach [28, 11, 33, 16] is to divide and conquer the problem by employing a person detector and performing single-person pose estimation for each detection.”
BackgroundMethodUsing k-Poselets for Detecting People and Localizing Their Keypoints
Gkioxari, G., Hariharan, B., Girshick, R., & Malik, J. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.458 · Cited by 175 · cited 2× · see citing works
“A common approach [28, 11, 33, 16] is to divide and conquer the problem by employing a person detector and performing single-person pose estimation for each detection.”
BackgroundMethodMulti-person Pose Estimation with Local Joint-to-Person Associations
Iqbal, U., & Gall, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_44 · Cited by 153 · cited 3× · see citing works
“A common approach [28, 11, 33, 16] is to divide and conquer the problem by employing a person detector and performing single-person pose estimation for each detection.”
BackgroundMethodInfluential
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He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
Deep High-Resolution Representation Learning for Human Pose Estimation
Sun, K., Xiao, B., Liu, D., & Wang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00584 · Cited by 5,984 · see citing works
“This paper is interested in single-person pose estimation, which is the basis of other related problems, such as multiperson pose estimation [6, 26, 32, 38, 46, 55, 40, 45, 17, 69],”
BackgroundObject Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545 · see citing works
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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“Skeleton and joint trajectories of human bodies are robust to illumination change and scene variation, and they are easy to obtain owing to the highly accurate depth sensors or pose estimation algorithms (Shotton et al. 2011; Cao et al. 2017a).”
BackgroundMethodOpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536 · see citing works
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“Ours (ResNet-50-FPN) is a single model that runs at 5 fps. CMU-Pose+++ [40] is the 2016 competition winner that uses multi-scale testing, post-processing with CPM [42], and filtering with an object detector, adding a cumulative (cid:6) 5 points (clarified in personal communication). y : G-RMI was…”
BackgroundMethodInfluentialBeyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
Sun, Y., Zheng, L., Yang, Y., Tian, Q., & Wang, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_30 · Cited by 2,660 · see citing works
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484 · see citing works
Simple Baselines for Human Pose Estimation and Tracking
Xiao, B., Wu, H., & Wei, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_29 · Cited by 2,233 · see citing works
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
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RMPE: Regional Multi-person Pose Estimation
Fang, H.-S., Xie, S., Tai, Y., & Lu, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.256 · Cited by 1,976 · see citing works
Cascaded Pyramid Network for Multi-person Pose Estimation
Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., & Sun, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00742 · Cited by 1,879 · see citing works
Expressive Body Capture: 3D Hands, Face, and Body From a Single Image
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Fast Underwater Image Enhancement for Improved Visual Perception
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Openface 2.0: facial behavior analysis toolkit
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Using DeepLabCut for 3D markerless pose estimation across species and behaviors
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BackgroundA Survey of Deep Learning-Based Object Detection
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,392 · see citing works
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Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., & Tian, Q. C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00371 · Cited by 1,285 · see citing works
Exploring the Limits of Weakly Supervised Pretraining
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Hand Keypoint Detection in Single Images Using Multiview Bootstrapping
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Self-citationCharacter Region Awareness for Text Detection
Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00959 · Cited by 1,160 · see citing works
“This heatmap representation has been used in other applications, such as in pose estimation works [1, 29] due to its high flexibility when dealing with ground truth regions that are not rigidly bounded.”
MethodBottleneck Transformers for Visual Recognition
Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01625 · Cited by 1,385 · see citing works
“Incorporating self-attention for other computer vision tasks such as keypoint detection [9] and 3D shape prediction [23]; studying self-attention architectures for self-supervised learning in computer vision [29, 26, 59, 11, 24, 12]; and scaling to much larger datasets such as JFT, YFCC and Instagram, are ripe avenues…”
BackgroundBottom-Up Object Detection by Grouping Extreme and Center Points
Zhou, X., Zhuo, J., & Krahenbuhl, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00094 · Cited by 1,088 · see citing works
“The network is trained in a fully supervised way, with either an L2 loss to a rendered Gaussian map [3,5,30,49] or with a per-pixel logistic regression loss [22, 34, 35].”
BackgroundMethodInfluentialMoCoGAN: Decomposing Motion and Content for Video Generation
Tulyakov, S., Liu, M.-Y., Yang, X., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00165 · Cited by 1,076 · see citing works
VIBE: Video Inference for Human Body Pose and Shape Estimation
Kocabas, M., Athanasiou, N., & Black, M. J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00530 · Cited by 1,065 · see citing works
“PennAction [64] and PoseTrack [3] are the only ground-truth 2D video datasets we use, while InstaVariety [30] and Kinetics-400 [13] are pseudo ground-truth datasets annotated using a 2D keypoint detector [12, 34].”
MethodRecovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera
von Marcard, T., Henschel, R. D., Black, M. J., Rosenhahn, B., & Pons‐Moll, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_37 · Cited by 1,062 · see citing works
Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop
Kolotouros, N., Pavlakos, G., Black, M. J., & Daniilidis, K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00234 · Cited by 1,046 · see citing works
Skeleton-Based Action Recognition With Directed Graph Neural Networks
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00810 · Cited by 979 · see citing works
“, Kinetics) or the pose estimation algorithms [4, 10].”
BackgroundMethodAn Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00132 · Cited by 973 · see citing works
“Besides, the Microsoft Kinect [37] and advanced human pose estimation algorithms [2] make it easier to gain skeleton data.”
BackgroundHigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T. S., & Zhang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00543 · Cited by 1,023 · see citing works
“By contrast, bottom-up methods [3, 30, 33, 22] start by localizing identity-free keypoints for all the persons in an input image through predicting heatmaps of different anatomical keypoints, followed by grouping them into person instances.”
BackgroundMethodInfluentialIntegral Human Pose Regression
Sun, X. N., Xiao, B., Wei, F., Liang, S., & Wei, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_33 · Cited by 921 · see citing works
SLEAP: A deep learning system for multi-animal pose tracking
Pereira, T., Tabris, N., Matsliah, A., Turner, D. M., Li, J., Ravindranath, S., Papadoyannis, E. S., Normand, E., Deutsch, D. S., Wang, Z. Y., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01426-1 · Cited by 984 · see citing works
“To generate PAFs from labeled data, the user must define a directed graph, which we refer to as the skeleton, that connects all body parts to be tracked.”
BackgroundMethodInfluentialTowards Accurate Multi-person Pose Estimation in the Wild
Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., & Murphy, K. P. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.395 · Cited by 901 · see citing works
Revisiting Skeleton-based Action Recognition
Duan, H., Zhao, Y., Chen, K., Lin, D., & Dai, B. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00298 · Cited by 902 · see citing works
“Compared to its 2D Bottom-Up counterparts [5, 8, 43], Top-Down methods obtain superior performance on standard benchmarks such as COCO-keypoints [35].”
MethodNeural Sign Language Translation
Camgöz, N. C., Hadfield, S., Koller, O., Ney, H., & Bowden, R. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00812 · Cited by 828 · see citing works
Probabilistic Regression for Visual Tracking
Danelljan, M., Van Gool, L., & Timofte, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00721 · Cited by 798 · see citing works
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
“Although top-down methods still dominate the leaderboard of public benchmark datasets like MS COCO 10 , they are usually slower than bottom-up methods [112].”
MethodAlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Fang, H.-S., Li, J., Tang, H., Xu, C., Zhu, H., Xiu, Y., Li, Y., & Lu, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3222784 · Cited by 779 · see citing works
“Openpose [17], [25] introduces Part Affinity Fields (PAFs) to encode association scores between body parts with individ-”
BackgroundMethodInfluential3D Human Pose Estimation with Spatial and Temporal Transformers
Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., & Ding, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01145 · Cited by 719 · see citing works
“OpenPose [3], CPN [6], AlphaPose [13], and HRNet [35] have been extensively used as the 2D pose detectors.”
BackgroundMethodFast animal pose estimation using deep neural networks
Pereira, T., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S. ‐., Murthy, M., & Shaevitz, J. W. (2018) · Nature Methods · doi:10.1038/s41592-018-0234-5 · Cited by 659 · see citing works
Mask-Guided Contrastive Attention Model for Person Re-identification
Song, C., Huang, Y., Ouyang, W., & Wang, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00129 · Cited by 687 · see citing works
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Zhang, P., Lan, C., Zeng, W.-J., Xing, J., Xue, J.-R., & Zheng, N. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00119 · Cited by 564 · see citing works
“Second, the advance of cost effective depth cameras [54] and pose estimation technology [32, 3, 36] makes the access of skeleton much easier.”
BackgroundVITON: An Image-Based Virtual Try-on Network
Han, X., Wu, Z., Wu, Z., Yu, R., & Davis, L. S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00787 · Cited by 677 · see citing works
Through-Wall Human Pose Estimation Using Radio Signals
Zhao, M., Li, T., Alsheikh, M. A., Tian, Y., Zhao, H., Torralba, A. B., & Katabi, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00768 · Cited by 676 · see citing works
Li, D., Rodríguez-Opazo, C., Yu, X., & Li, H. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093512 · Cited by 700 · see citing works
“Some works [23, 10] also employ the joint locations as a guidance to extract local deep features around the joint regions.”
MethodChan, C., Ginosar, S., Zhou, T., & Efros, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00603 · Cited by 737 · see citing works
Deep learning in video multi-object tracking: A survey
Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665 · see citing works
“Such CNN used the maps extracted by PafNet, and was trained using reinforcement learning.”
MethodMulti-animal pose estimation, identification and tracking with DeepLabCut
Lauer, J., Zhou, M., Ye, S., Menegas, W., Schneider, S., Nath, T., Rahman, M. M., Santo, V. D., Soberanes, D., Feng, G., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01443-0 · Cited by 665 · see citing works
Papandreou, G., Zhu, T., Chen, L.-C., Gidaris, S., Tompson, J., & Murphy, K. P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_17 · Cited by 645 · see citing works
CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark
Li, J., Wang, C., Zhu, H., Mao, Y., Fang, H.-S., & Lu, C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01112 · Cited by 640 · see citing works
Colyer, S., Evans, M., Cosker, D. P., & Salo, A. (2018) · Sports Medicine - Open · doi:10.1186/s40798-018-0139-y · Cited by 635 · see citing works
Detecting and Recognizing Human-Object Interactions
Gkioxari, G., Girshick, R., Dollár, P., & He, K. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00872 · Cited by 624 · see citing works
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3028207 · Cited by 619 · see citing works
“[20] estimate the locations of 18 joints on every frame of the clips using the publicly available OpenPose toolbox [44].”
MethodTune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
Wu, J. Z., Ge, Y., Wang, X., Lei, S. W., Gu, Y., Shi, Y., Hsu, W., Shan, Y., Qie, X., & Shou, M. Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00701 · Cited by 622 · see citing works
“Note that the human pose sequence can be automatically detected from real-world videos using an off-the-shelf pose estimation model [4].”
Sign Language Recognition: A Deep Survey
Rastgoo, R., Kiani, K., & Escalera, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113794 · Cited by 609 · see citing works
Part-Aligned Bilinear Representations for Person Re-identification
Suh, Y., Wang, J., Tang, S., Mei, T., & Lee, K. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_25 · Cited by 575 · see citing works
YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
Maji, D., Nagori, S., Mathew, M., & Poddar, D. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00297 · Cited by 598 · see citing works
Zhou, X., Wang, D., & Krähenbühl, P. (2019) · arXiv · doi:10.48550/arxiv.1904.07850 · Cited by 288 · see citing works
PifPaf: Composite Fields for Human Pose Estimation
Kreiss, S., Bertoni, L., & Alahi, A. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01225 · Cited by 574 · see citing works
“We compare our proposed PifPaf method against the reproducible state-of-the-art bottom-up OpenPose [3] and top-down Mask R-CNN [18] methods.”
MethodInfluentialConvolutional Mesh Regression for Single-Image Human Shape Reconstruction
Kolotouros, N., Pavlakos, G., & Daniilidis, K. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00463 · Cited by 556 · see citing works
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
Wang, G., Yang, S., Liu, H., Wang, Z., Yang, Y., Wang, S., Yu, G., Zhou, E., & Sun, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00648 · Cited by 545 · see citing works
Features for Multi-target Multi-camera Tracking and Re-identification
Ristani, E., & Tomasi, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00632 · Cited by 556 · see citing works
Learning Feature Pyramids for Human Pose Estimation
Yang, W. P., Li, S., Ouyang, W., Li, H., & Wang, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.144 · Cited by 547 · see citing works
DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife · doi:10.7554/elife.47994 · Cited by 547 · see citing works
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
Song, Y., Zhang, Z., Shan, C., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3157033 · Cited by 544 · see citing works
“Skeleton data is a time series of 3D coordinates of multiple skeleton joints, which can be either estimated from 2D images by pose estimation methods [5] or directly collected by multimodal sensors such as Kinect [6].”
Learning 3D Human Dynamics From Video
Kanazawa, A., Zhang, J. Y., Felsen, P., & Malik, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00576 · Cited by 533 · see citing works
“The final output of the temporal encoder has the same feature dimension as φ .”
MethodNeural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., & Schiele, B. (2018) · 2018 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00062 · Cited by 498 · see citing works
Attention-Aware Compositional Network for Person Re-identification
Xu, J., Zhao, R., Zhu, F., Wang, H., & Ouyang, W. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00226 · Cited by 512 · see citing works
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
Joo, H., Simon, T., & Sheikh, Y. A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00868 · Cited by 498 · see citing works
Self-citationA Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503 · see citing works
Deformable GANs for Pose-Based Human Image Generation
Siarohin, A., Sangineto, E., Lathuilière, S., & Sebe, N. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00359 · Cited by 512 · see citing works
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Andriluka, M., Iqbal, U., Insafutdinov, E., Pishchulin, L., Milan, A., Gall, J., & Schiele, B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00542 · Cited by 501 · see citing works
Li, Z., Wang, G., & Ji, X. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00777 · Cited by 485 · see citing works
“[19, 29] predicted semantic keypoints for pose and viewpoint estimation with an hourglassbased network [17, 5].”
BackgroundPose-Normalized Image Generation for Person Re-identification
Qian, X., Fu, Y., Xiang, T., Wang, W., Qiu, J., Wu, Y., Jiang, Y., & Xue, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_40 · Cited by 484 · see citing works
Survey on Emotional Body Gesture Recognition
Noroozi, F., Corneanu, C. A., Kamińska, D., Sapiński, T., Escalera, S., & Anbarjafari, G. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2874986 · Cited by 491 · see citing works
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., & Torralba, A. B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00701 · Cited by 480 · see citing works
“As evidenced by the success of 2D body and face tracking models in the wild [2], to improve in-the-wild robustness it is important to collect data with a large number of different subjects, large variation in natural illumination and a wide range of head poses and gaze directions.”
BackgroundVideo Based Reconstruction of 3D People Models
Alldieck, T., Magnor, M. A., Xu, W., Theobalt, C., & Pons‐Moll, G. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00875 · Cited by 468 · see citing works
A Variational U-Net for Conditional Appearance and Shape Generation
Esser, P., & Sutter, E. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00923 · Cited by 465 · see citing works
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection
Liu, W., Liao, S., Ren, W., Hu, W., & Yu, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00533 · Cited by 463 · see citing works
“Previously, FCN has already been applied to and made a success in multi-person pose estimation [5, 34], where several keypoints are firstly detected merely through responses of full convolutions, and then they are further grouped into complete poses of individual persons.”
MethodDeep learning tools for the measurement of animal behavior in neuroscience
Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.008 · Cited by 450 · see citing works
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Xu, R., Chen, Z., Zuo, W., Yan, J., & Lin, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00417 · Cited by 448 · see citing works
Gkioxari, G., Johnson, J., & Malik, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00988 · Cited by 468 · see citing works
“We can now build systems that accurately recognize objects [19, 29, 54, 60], localize them with 2D bounding boxes [13, 46] or masks [18], and predict 2D keypoint positions [3, 18, 64] in cluttered, real-world images.”
Monocular human pose estimation: A survey of deep learning-based methods
Chen, Y., Tian, Y., & He, M. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102897 · Cited by 455 · see citing works
“By consolidating OpenPose (Cao et al., 2016) the architecture estimated and rendered 3D human pose in real-time using only CPU processing.”
BackgroundMethodInfluentialSkeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
Si, C., Jing, Y., Wang, W., Wang, L., & Tan, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_7 · Cited by 421 · see citing works
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Geng, Z., Sun, K., Xiao, B., Zhang, Z., & Wang, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01444 · Cited by 418 · see citing works
“Various grouping techniques are developed, such as part-affinity fields in OpenPose [7] and its extension in Pif-Paf [31], associative embedding [40], greedy decoding with hough voting in PersonLab [47], and graph clustering in HGG [27].”
BackgroundMethodInfluentialOrdinal Depth Supervision for 3D Human Pose Estimation
Pavlakos, G., Zhou, X., & Daniilidis, K. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00763 · Cited by 413 · see citing works
3D Human Pose Estimation in the Wild by Adversarial Learning
Yang, W. P., Ouyang, W., Wang, X., Ren, J., Li, H., & Wang, X. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00551 · Cited by 404 · see citing works
Pose Transferrable Person Re-identification
Liu, J., Ni, B., Yan, Y., Zhou, P., Cheng, S., & Hu, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00431 · Cited by 400 · see citing works
Sun, Y., Xu, Q., Li, Y., Zhang, C., Li, Y., Wang, S., & Sun, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00048 · Cited by 406 · see citing works
“[23] extract several region parts, with pose estimation [17, 27, 10, 18, 1], human parsing [2, 5] and uniform partitioning, respectively.”
Backgroundmm-Pose: Real-Time Human Skeletal Posture Estimation Using mmWave Radars and CNNs
Sengupta, A., Jin, F., Zhang, R., & Cao, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2991741 · Cited by 406 · see citing works
“Another extremely popular bottom-up approach for human pose estimation is OpenPose , proposed by researchers at Carneigie Mellon University in 2017 [18].”
Deep 3D human pose estimation: A review
Wang, J., Tan, S., Zhen, X., Xu, S., Zheng, F., He, Z., & Shao, L. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103225 · Cited by 406 · see citing works
Single-Shot Multi-person 3D Pose Estimation from Monocular RGB
Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons‐Moll, G., & Theobalt, C. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00024 · Cited by 395 · see citing works
Progressive Pose Attention Transfer for Person Image Generation
Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., & Bai, X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00245 · Cited by 386 · see citing works
“We adopt HPE [3] as pose joints detector and filter out images where no human body is detected.”
MethodBodyNet: Volumetric Inference of 3D Human Body Shapes
Varol, G., Ceylan, D., Russell, B. C., Yang, J., Yumer, E., Laptev, I., & Schmid, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_2 · Cited by 420 · see citing works
Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple Video Cameras
Nakano, N., 櫻, 哲., Ueda, K., Omura, L., Kimura, A., Iino, Y., Fukashiro, S., & Yoshioka, S. (2020) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2020.00050 · Cited by 384 · see citing works
- Authors
- Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1302-1310
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2559085405
- Cited by
- 7,539
- References
- 32
- Citations by year
- 2026: 384 · 2025: 774 · 2024: 968 · 2023: 1007 · 2022: 913 · 2021: 1016 · 2020: 1072 · 2019: 930 · 2018: 424 · 2017: 50
- FWCI
- 197.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- multi-person pose estimation, part affinity fields, real-time pose estimation, bottom-up parsing, keypoint detection
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