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Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Abstract
We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.
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“6M” [10] that includes images and 3D poses of people but are captured in the controlled indoor environments, whereas our dataset includes real-world images but provides 2D poses only.”
BackgroundSUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943
“There are many existing RGB-D datasets available [54] [47] [1] [25] [44] [60] [49] [45] [29] [66] [57] [52] [46] [16] [21] [73] [35] [67] [3] [41] [10] [63] [42] [64] [65] [48] [12] [33] [8] [50] [26] [6].”
End-to-End Recovery of Human Shape and Pose
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Learning from Simulated and Unsupervised Images through Adversarial Training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (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.2017.241 · Cited by 1,888
Expressive Body Capture: 3D Hands, Face, and Body From a Single Image
Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., & Black, M. J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01123 · Cited by 1,781
“6M [10], and PosePrior [2] as our dataset.”
BackgroundKeep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_34 · Cited by 1,502
“We perform the same analysis on the Human 3.6M dataset [18], which has a wider range of poses.”
MethodInfluentialA Simple Yet Effective Baseline for 3d Human Pose Estimation
Martínez, J., Hossain, R., Romero, J., & Little, J. J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.288 · Cited by 1,504
DensePose: Dense Human Pose Estimation in the Wild.
Güler, R. A., Neverova, N., & Kokkinos, I. (2018) · UCL Discovery (University College London) · Cited by 1,421
3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training
Pavllo, D., Feichtenhofer, C., Grangier, D., & Auli, M. (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.2019.00794 · Cited by 1,378
“Before the success of deep learning, most approaches to 3D pose estimation were based on feature engineering and assumptions about skeletons and joint mobility [48, 42, 20, 18].”
BackgroundMehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H., Xu, W., Casas, D., & Theobalt, C. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073596 · Cited by 1,186
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133
“The applications include: (i) modeling human motion [14] from motion capture data [21]; (ii) human activity detection and anticipation [29, 31]; and (iii) maneuver anticipation from real-world driving data [22].”
Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision
Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., & Theobalt, C. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00064 · Cited by 1,149
VIBE: Video Inference for Human Body Pose and Shape Estimation
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Recovering 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
“6M [8], which provide synchronized video with MoCap.”
InfluentialLearning 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 1,081
“benchmarks, where the models tend to overfit [11, 22].”
BackgroundRomero (2017) · doi:10.1145/3130800.3130883 · Cited by 1,056
On Human Motion Prediction Using Recurrent Neural Networks
Martínez, J., Black, M. J., & Romero, J. (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.2017.497 · Cited by 1,019
Integral 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 943
Learning from Synthetic Humans
Varol, G., Romero, J., Martín, X., Mahmood, N., Black, M. J., Laptev, I., & Schmid, C. (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.2017.492 · Cited by 915
“n poses, clothing, hair styles, body shapes, occlusions, viewpoints, motion blur and other factors. Much of these variations, however, can be synthesized using existing 3D motion capture (MoCap) data [3,16] and modern tools for realistic rendering. Provided sufficient realism, such an approach would be WILLOW project-t…”
BackgroundMethodInfluentialTowards Understanding Action Recognition
Jhuang, H., Gall, J., Zuffi, S., Schmid, C., & Black, M. J. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.396 · Cited by 906
“While marker-based motion capture systems provide accurate 3D ground-truth pose data [12, 15, 19, 25], they are impractical for recording realistic video data.”
BackgroundRecurrent Network Models for Human Dynamics
Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.494 · Cited by 933
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
Pavlakos, G., Zhou, X., Derpanis, K. G., & Daniilidis, K. (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.2017.139 · Cited by 885
Human Pose Estimation with Iterative Error Feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.512 · Cited by 805
3D 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
“6M [3] test set S11 with Eating and Photo actions.”
BackgroundEnd-to-End Human Pose and Mesh Reconstruction with Transformers
Lin, K., Wang, L., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00199 · Cited by 740
“Mean-Per-Joint-Position-Error (MPJPE) [21] is a metric for evaluating human 3D pose [22, 24, 8].”
BackgroundInfluentialDeep Predictive Coding Networks for Video Prediction and Unsupervised LearningPreprint
Lotter, W. E., Kreiman, G., & Cox, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08104 · Cited by 904
“Brabandere et al. (2016). Our PredNet model outperforms the model by Brabandere et al. (2016) by 29%. Details can be found in Appendix 5.2. Also in Appendix 5.2, we present results for the Human3.6M (Ionescu et al., 2014) dataset, as reported by Finn et al. (2016). Without re-optimizing hyperparameters, our 8 Publishe…”
MethodInfluentialColyer, S., Evans, M., Cosker, D. P., & Salo, A. (2018) · Sports Medicine - Open · doi:10.1186/s40798-018-0139-y · Cited by 713
“Two such benchmarks are the widely used HumanEva dataset [111] and the more recent Human 3.6M dataset [112].”
MethodSemantic Graph Convolutional Networks for 3D Human Pose Regression
Zhao, L., Peng, X., Tian, Y., Kapadia, M., & Metaxas, D. N. (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.2019.00354 · Cited by 631
“6M [24] is currently the largest publicly available dataset for 3D human pose estimation.”
BackgroundMethodInfluentialLearning to Estimate 3D Human Pose and Shape from a Single Color Image
Pavlakos, G., Zhu, L., 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.2018.00055 · Cited by 602
Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach
Zhou, X., Qi-xing Huang, A., Sun, X. N., Xue, X., & Wei, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.51 · Cited by 620
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 629
Generating Diverse and Natural 3D Human Motions from Text
Guo, C., Zou, S., Zuo, X., Wang, S., Ji, W., Li, X., & Cheng, L. (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/cvpr52688.2022.00509 · Cited by 582
“6M [15], MoVi [9] and BABEL [33], in the form of everyday actions and sports movements.”
BackgroundCompositional Human Pose Regression
Sun, X. N., Shang, J., Liang, S., & Wei, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.284 · Cited by 587
“6M [20] is the largest 3D human pose benchmark.”
BackgroundMethodInfluentialMultimodal Deep Autoencoder for Human Pose Recovery
Hong, C., Yu, J., Wan, J., Tao, D., & Wang, M. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2487860 · Cited by 570
3D Human Pose Estimation = 2D Pose Estimation + Matching
Chen, C.-H., & Ramanan, D. (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.2017.610 · Cited by 569
“We follow standard train/test protocol splits. but our analysis reveals that there has been inconsistent reporting in the literature, both in terms of test sets and evaluation criteria.”
BackgroundMethodInfluentialConvolutional 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 574
“6M [10] and UP-3D [18], while we evaluate our approach on Human3.”
Method2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Luvizon, D. C., Picard, D., & Tabia, H. (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.2018.00539 · Cited by 560
Unite the People: Closing the Loop Between 3D and 2D Human Representations
Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M. J., & Gehler, 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.2017.500 · Cited by 533
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01315 · Cited by 553
“While there are many large scale 3D motion capture datasets [39, 59, 1, 37], mocap dataset of 3D dance is quite limited as it requires heavy instrumentation and expert dancers for capture.”
BackgroundMethodLearning Trajectory Dependencies for Human Motion Prediction
Mao, W., Liu, M.-M., Salzmann, M., & Li, H. (2019) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2019.00958 · Cited by 591
“For the coordinate-based representation, we adopt the standard body model of [10] to convert the joint angles to 3D coordinates.”
BackgroundMethodInfluentialLearning 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
Neural 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
Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., & Yu, P. S. (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.2019.00937 · Cited by 536
“6M dataset [13] contains human actions of 17 scenarios, including 3.”
BackgroundExploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks
Cai, Y., Ge, L., Liu, J., Cai, J., Cham, T., Yuan, J., & Magnenat‐Thalmann, N. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00236 · Cited by 522
“6M dataset [18] is a largescale and commonly used dataset for 3D human pose estimation, which consists of 3.”
BackgroundMethodInfluentialLook into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing
Gong, K., Liang, X., Zhang, D., Shen, X., & Lin, L. (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.2017.715 · Cited by 529
“Some other datasets in the vision community were dedicated to the tasks of clothes recognition, retrieval [20, 23] and human pose estimation [1, 12], while our LIP dataset only focuses on human parsing.”
BackgroundLifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
Tomè, D., Russell, C., & Agapito, L. (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.2017.603 · Cited by 505
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 523
Action-Conditioned 3D Human Motion Synthesis with Transformer VAE
Petrovich, M., Black, M. J., & Varol, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01080 · Cited by 566
“A challenge specific to our action-condition generation problem is that there exists limited motion capture (MoCap) data paired with distinct action labels, typically on the order of 10 categories [29, 59].”
BackgroundMHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
Li, W., Liu, H., Tang, H., Wang, P., & Gool, L. V. (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/cvpr52688.2022.01280 · Cited by 465
“6M dataset [12] is the largest and most representative benchmark for 3D HPE.”
BackgroundLearnable Triangulation of Human Pose
Iskakov, K., Burkov, E., Lempitsky, V., & Malkov, Y. A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00781 · Cited by 448
3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network
Li, S., & Chan, A. B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_23 · Cited by 460
“3D Human Pose Estimation from Monocular Images with Deep CNN 5 positions with respect to root joint, which is a common formulation as in [12, 9, 11], we instead aim to predict the joint positions relative to their parents joints,”
BackgroundMethodInfluentialMonocular 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 479
“Datasets play an important role in deep learning-baed human pose estimation.”
BackgroundMethodInfluentialMotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model
Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3355414 · Cited by 828
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., & Liu, Z. (2022) · arXiv · doi:10.48550/arxiv.2208.15001 · Cited by 437
MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video
Zhang, J., Tu, Z., Yang, J., Chen, Y., & Yuan, J. (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/cvpr52688.2022.01288 · Cited by 457
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 467
“• basic human-related tasks are very important in practice, so there already exist large real datasets for face recognition [84, 220, 355], pose estimation [24, 274, 373, 600], and other problems, which often limits synthetic”
BackgroundSimVP: Simpler yet Better Video Prediction
Gao, Z., Tan, C., Wu, L., & Li, S. Z. (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/cvpr52688.2022.00317 · Cited by 466
“6 [24] is a complex human pose dataset with 3.6 million samples, recording different activities.”
BackgroundInfluentialPARE: Part Attention Regressor for 3D Human Body Estimation
Kocabas, M., Huang, C.-H. P., Hilliges, O., & Black, M. J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01094 · Cited by 421
Exploiting Temporal Information for 3D Human Pose Estimation
Hossain, M. R. I., & Little, J. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_5 · Cited by 435
“6M dataset [29] under protocol #1 are shown in Table 1.”
BackgroundMethodInfluentialDeep Representation Learning for Human Motion Prediction and Classification
Bütepage, J., Black, M. J., Kragić, D., & Kjellström, H. (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.2017.173 · Cited by 435
“6M dataset [12], which are preprocessed as described above.”
MethodSparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
Zhou, X., Zhu, M., Leonardos, S., Derpanis, K. G., & Daniilidis, K. (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.2016.537 · Cited by 414
Ordinal 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 422
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 413
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 436
Li, J., Xu, C., Chen, Z., Bian, S., Yang, L., & Lu, C. (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/cvpr46437.2021.00339 · Cited by 404
“6M [16] can be obtained by a linear combination of the mesh vertices through a linear regressor W , i.”
Background3D skeleton-based human action classification: A survey
Presti, L. L., & Cascia, M. L. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.11.019 · Cited by 393
“6M [ 147], 2 which is providing an impressive mole of data with the main goal of testing body pose estimation methods.”
BackgroundWade, L., Needham, L., McGuigan, P. M., & Bilzon, J. (2022) · PeerJ · doi:10.7717/peerj.12995 · Cited by 407
3D Human Pose Estimation from a Single Image via Distance Matrix Regression
Moreno-Noguer, F. (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.2017.170 · Cited by 397
“6M [17], are two popular MoCap datasets used for this purpose.”
BackgroundAnimatable Neural Radiance Fields for Modeling Dynamic Human Bodies
Peng, S., Dong, J., Wang, Q., Zhang, S., Shuai, Q., Zhou, X., & Bao, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01405 · Cited by 423
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
Choi, H., Moon, G., & Lee, K. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_45 · Cited by 410
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 407
“The relative robustness of 3DPCK over MPJPE[18] is also useful to offset the effect of jitter that arises in all non-synthetic annotations, including ours.”
BackgroundInfluentialBodyNet: 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
Moon, G., & Lee, K. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_44 · Cited by 385
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction
Li, M., Chen, S.-H., Zhao, Y., Zhang, Y., Wang, Y.-F., & Tian, Q. (2020) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00029 · Cited by 382
“6M dataset [19] has 7 subjects performing 15 different classes of actions.”
BackgroundDeepHuman: 3D Human Reconstruction From a Single Image
Zheng, Z., Yu, T., Wei, Y., Dai, Q., & Liu, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00783 · Cited by 359
TEMOS: Generating Diverse Human Motions from Textual Descriptions
Petrovich, M., Black, M. J., & Varol, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_28 · Cited by 429
Convolutional Sequence to Sequence Model for Human Dynamics
Li, C., Zhang, Z., Lee, W. S., & Lee, G. H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00548 · Cited by 364
Tewari, A., Thies, J., Mildenhall, B., Srinivasan, P. P., Tretschk, E., Yifan, W., Lassner, C., Sitzmann, V., Martin-Brualla, R., Lombardi, S., et al. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14507 · Cited by 439
MotionBERT: A Unified Perspective on Learning Human Motion Representations
Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., & Wang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01385 · Cited by 416
“6M [38] and 3DPW [113] datasets and additionally add COCO [61] dataset during training following [46, 58, 114].”
BackgroundMethodInfluentialGHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models
Xu, H., Băzăvan, E. G., Zanfir, A., Freeman, W. T., Sukthankar, R., & Sminchisescu, C. (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/cvpr42600.2020.00622 · Cited by 373
“Many image and video-based pose and shape estimation methods have also been proposed[33, 27, 25, 40, 22, 1, 12, 26, 34, 23].”
BackgroundSelf-citationCamera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image
Moon, G., Chang, J. Y., & Lee, K. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01023 · Cited by 359
“6M dataset [16] is the largest 3D single-person pose benchmark.”
BackgroundMethodInfluentialPyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., & Sun, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01125 · Cited by 357
“6M [3], MPI-INF-3DHP [13], LSP [4], LSP-Extended [5], MPII [1], COCO [11].”
MethodInfluentialHuang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., & Pons-Moll, G. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275108 · Cited by 342
Anatomy-Aware 3D Human Pose Estimation With Bone-Based Pose Decomposition
Chen, T., Fang, C., Shen, X., Zhu, Y., Chen, Z., & Luo, J. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3057267 · Cited by 356
The Pose Knows: Video Forecasting by Generating Pose Futures
Walker, J., Marino, K., Gupta, A., & Hebert, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.361 · Cited by 341
“e Evaluation First we evaluate how well our Pose-VAE is able to forecast actions in pose space. There has been some prior work [10, 17] on forecasting pose in mocap datasets such as the H3.6M dataset [15]. However, to the best of our knowledge there has been no evaluation on 2D pose forecasting on unconstrained, reali…”
MethodMonocular Total Capture: Posing Face, Body, and Hands in the Wild
Xiang, D., Joo, H., & Sheikh, Y. 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.2019.01122 · Cited by 332
“6M [22] is an indoor markerbased human MoCap dataset, and currently the most commonly used benchmark for 3D body pose estimation.”
BackgroundMethodLin, K., Wang, L., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01270 · Cited by 361
“6M [12], MuCo-3DHP [25], UP-3D [21], COCO [23], MPII [1].”
BackgroundETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation
Zhang, X., Park, S., Beeler, T., Bradley, D., Tang, S., & Hilliges, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_22 · Cited by 334
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
Fang, H.-S., Xu, Y., Wang, W., Liu, X., & Zhu, S. C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12270 · Cited by 338
“6M (Ionescu et al. 2014), deep learning based methods have obtained encouraging success.”
BackgroundMethodInfluentialSpeech gesture generation from the trimodal context of text, audio, and speaker identity
Yoon, Y., Cha, B., Lee, J.-H., Jang, M., Lee, J., Kim, J., & Lee, G. (2020) · ACM Transactions on Graphics · doi:10.1145/3414685.3417838 · Cited by 360
“6M dataset (Ionescu et al. 2013) containing motion capture data of 7 dierent actors and 17 dierent scenarios including discussion and making purchases showing co-speech gestures.”
BackgroundHP-GAN: Probabilistic 3D Human Motion Prediction via GAN
Barsoum, E., Kender, J. R., & Liu, Z. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00191 · Cited by 344
3D Human pose estimation: A review of the literature and analysis of covariates
Sarafianos, N., Boteanu, B. A., Ionescu, B. E., & Kakadiaris, I. A. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.09.002 · Cited by 342
Anipose: A toolkit for robust markerless 3D pose estimation
Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324
History Repeats Itself: Human Motion Prediction via Motion Attention
Mao, W., Liu, M., & Salzmann, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_28 · Cited by 322
“functions we use to train our model on either 3D coordinates or joint angles. For 3D joint coordinates prediction, following [20], we make use of the Mean Per Joint Position Error (MPJPE) proposed in [10]. In particular, for one training sample, this yields the loss ‘= 1 J(M+ T) MX+T t=1 XJ j=1 kp^t;j pt;j k 2 ; (6) w…”
MethodInfluentialExploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation
Li, W., Liu, H., Ding, R., Liu, M., Wang, P., & Yang, W. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3141231 · Cited by 323
Space-time representation of people based on 3D skeletal data: A review
Han, F., Reily, B., Hoff, W., & Zhang, H. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.01.011 · Cited by 316
Quantifying behavior to understand the brain
Pereira, T., Shaevitz, J. W., & Murthy, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00734-z · Cited by 323
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
von Marcard, T., Rosenhahn, B., Black, M. J., & Pons‐Moll, G. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13131 · Cited by 309
Metadata
- Authors
- Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C.
- Affiliations
- IMAR - Institutul de Matematică „Simion Stoilow” al Academiei Române (RO)
- Lund University (SE)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-07
- Volume
- 36
- Issue
- 7
- Pages
- 1325-1339
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2101032778
- Cited by
- 3,653
- References
- 71
- Citations by year
- 2026: 349 · 2025: 529 · 2024: 581 · 2023: 543 · 2022: 390 · 2021: 429 · 2020: 305 · 2019: 235 · 2018: 133 · 2017: 78 · 2016: 48 · 2015: 20 · 2014: 10
- FWCI
- 46.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Funders
- Universitatea Politehnica din București
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- 3D human pose estimation, natural environment, 3D body scans, occlusion, human activities
- MeSH terms
- Algorithms, Biometry, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Photography, Posture, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Databases, Factual, Information Storage and Retrieval, Ecosystem, Imaging, Three-Dimensional, Whole Body Imaging
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