Lifting Monocular Events to 3D Human Poses
Open accessAbstract
This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint; each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://iit-pavis.github.io/lifting_events_to_3d_hpe.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The proposed monocular HPE performance is lower than LiftMono by 5.96 MPJPE(mm).
“However, for monocular settings, our HPE performance score is lower than [3]; the difference is 5.96 MPJPE(mm).”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [3]
Table 1 (Discussion and Conclusions; page not available) reports the cited paper’s monocular MPJPE results, including 92.09 mm for its three-stage constant-count model. The paper does not report the citing authors’ HPE score, so its text alone cannot establish the stated 5.96 mm difference. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn the 3D HPE monocular comparison, LiftMono has an MPJPE of 95.51 mm and the proposed privacy method has an MPJPE of 101.47 mm.
“| LiftMono [3] | monocular | 95.51 |”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [3]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634 · see citing works
OpenPose: 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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734 · see citing works
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653 · cited 1× · see citing works
“6m dataset [19, 20] and compare our approach with standard RGB methods to establish a strong benchmark for further research.”
MethodGallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566 · see citing works
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485 · cited 1× · see citing works
“This observation pushed the research community and engineers to develop new sensors, event-cameras, with a neuromorphic inspiration that provide crucial advantages in time-critical tasks and applications [30, 4].”
BackgroundNonverbal communication in human interaction
Knapp, M. L. (1972) · Cited by 2,209 · see citing works
End-to-End Recovery of Human Shape and Pose
Kanazawa, A., Black, M. J., Jacobs, D. W., & Malik, 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.2018.00744 · Cited by 1,899 · see citing works
Keep 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 · see citing works
Mehta, 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 1,156 · see citing works
A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
Brändli, C., Berner, R., Yang, M., Liu, S., & Delbrück, T. (2014) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2014.2342715 · Cited by 1,149 · see citing works
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 · see citing works
Taskonomy: Disentangling Task Transfer Learning
Zamir, A. R., Sax, A., Shen, W., Guibas, L., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00391 · Cited by 1,128 · see citing works
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 · see citing works
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Maqueda, A. I., Loquercio, A., Gallego, G., Garcia, N., & Scaramuzza, 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.2018.00568 · Cited by 624 · see citing works
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E., & Benosman, R. B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2574707 · Cited by 617 · see citing works
Mueggler, E., Rebecq, H., Gallego, G., Delbrück, T., & Scaramuzza, D. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917691115 · Cited by 612 · see citing works
What makes ImageNet good for transfer learning?Preprint
Huh, M., Agrawal, P., & Efros, A. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.08614 · Cited by 591 · see citing works
Gallego, G., Delbrück, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000521357 · Cited by 590 · see citing works
2D/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 · see citing works
Stacked Hourglass Networks for Human Pose EstimationPreprint
Newell, A., Yang, K., & Deng, J. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06937 · Cited by 531 · see citing works
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R. 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.2018.00186 · Cited by 496 · see citing works
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Maqueda, A. I., Loquercio, A., Gallego, G., Garcia, N. N., & Scaramuzza, D. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-175994 · Cited by 493 · see citing works
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
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera
Kim, H., Leutenegger, S., & Davison, A. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_21 · Cited by 438 · see citing works
A Study of Vicon System Positioning Performance
Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., & Savatier, X. (2017) · Sensors · doi:10.3390/s17071591 · Cited by 438 · see citing works
End-to-End Learning of Representations for Asynchronous Event-Based Data
Gehrig, D., Loquercio, A., Derpanis, K. G., & Scaramuzza, D. (2019) · Zurich Open Repository and Archive (University of Zurich) · doi:10.1109/iccv.2019.00573 · Cited by 401 · see citing works
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Neil, D., Pfeiffer, M., & Liu, S. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-149394 · Cited by 361 · see citing works
ESIM: an Open Event Camera Simulator
Rebecq, H., Gehrig, D., & Scaramuzza, D. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-175993 · Cited by 351 · see citing works
EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time
Rebecq, H., Gallego, G., Mueggler, E., & Scaramuzza, D. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1050-6 · Cited by 279 · see citing works
Resolving 3D Human Pose Ambiguities With 3D Scene Constraints
Hassan, M. S., Choutas, V., Tzionas, D., & Black, M. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00237 · Cited by 275 · see citing works
Mahowald, M. A., & Mead, C. (1991) · Scientific American · doi:10.1038/scientificamerican0591-76 · Cited by 273 · see citing works
Latent structured models for human pose estimation
Ionescu, C., Li, F., & Sminchisescu, C. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126500 · Cited by 262 · see citing works
Li, B., Dai, Y., Cheng, X., Chen, H., Lin, Y., & He, M. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026282 · Cited by 252 · see citing works
Fast Image Reconstruction with an Event Camera
Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N. M., Mahony, R. E., & Scaramuzza, D. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093366 · Cited by 252 · see citing works
Xu, W., Chatterjee, A., Zollhöfer, M., Rhodin, H., Mehta, D., Seidel, H., & Theobalt, C. (2018) · ACM Transactions on Graphics · doi:10.1145/3181973 · Cited by 235 · see citing works
Towards Accurate Marker-Less Human Shape and Pose Estimation over Time
Huang, Y., Bogo, F., Lassner, C., Kanazawa, A., Gehler, P., Romero, J., Akhter, I., & Black, M. J. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00055 · Cited by 217 · see citing works
Unsupervised 3D Pose Estimation With Geometric Self-Supervision
Chen, C.-H., Tyagi, A., Agrawal, A., Drover, D., Rohith, M. V., Stojanov, S., & Rehg, J. 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.2019.00586 · Cited by 194 · see citing works
Gehrig, D., Rüegg, M., Gehrig, M., Hidalgo‐Carrió, J., & Scaramuzza, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3060707 · Cited by 169 · see citing works
Event-Based Motion Segmentation by Motion Compensation
Stoffregen, T., Gallego, G., Drummond, T. W., Kleeman, L., & Scaramuzza, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00734 · Cited by 159 · see citing works
A real-time tracker for markerless augmented reality
Comport, A. I., Marchand, É., & Chaumette, F. (2003) · doi:10.5555/946248.946787 · Cited by 157 · see citing works
Body Language and the Social Order: Communication As Behavioral Control
Scheflen, A. E., & Scheflen, A. (1972) · Bibliothèque et Archives nationales du Québec (Québec government) · Cited by 145 · see citing works
AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance
Wang, J., Qiu, K., Peng, H., Fu, J., & Zhu, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350910 · Cited by 143 · see citing works
3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training
Cheng, Y., Yang, B., Wang, B., & Tan, R. T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6689 · Cited by 135 · see citing works
Time-Ordered Recent Event (TORE) Volumes for Event Cameras
Baldwin, R. W., Liu, R., Almatrafi, M., Asari, V. K., & Hirakawa, K. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3172212 · Cited by 127 · see citing works
EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera
Xu, L., Xu, W., Golyanik, V., Habermann, M., Fang, L., & Theobalt, 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/cvpr42600.2020.00502 · Cited by 116 · see citing works
Unsupervised Event-Based Optical Flow Using Motion Compensation
Zhu, A. Z., Yuan, L., Chaney, K. P., & Daniilidis, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_54 · Cited by 107 · see citing works
A real-time tracker for markerless augmented reality
Comport, A. I., Marchand, É., & Chaumette, F. (2004) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2003.1240686 · Cited by 106 · see citing works
DHP19: Dynamic Vision Sensor 3D Human Pose Dataset
Calabrese, E., Taverni, G., Easthope, C. A., Skriabine, S., Corradi, F., Longinotti, L., Eng, K., & Delbruck, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00217 · Cited by 99 · see citing works
A massively asynchronous, parallel brain
Zeki, S. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0174 · Cited by 66 · see citing works
Towards Accurate Markerless Human Shape and Pose Estimation over TimePreprint
Huang, Y., Bogo, F., Lassner, C., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07548 · Cited by 65 · see citing works
Wang, J., Qiu, K., Peng, H., Fu, J., & Zhu, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350609 · Cited by 53 · see citing works
Li, B., He, M., Cheng, X., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.05645 · Cited by 51 · see citing works
Markerless motion capture of man-machine interaction
Rosenhahn, B., Schmaltz, C., Brox, T., Weickert, J., Cremers, D., & Seidel, H. (2008) · 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.2008.4587520 · Cited by 50 · see citing works
High Speed and High Dynamic Range Video with an Event Camera
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2963386 · Cited by 50 · see citing works
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
Hasan, I., Setti, F., Tsesmelis, T., Belagiannis, V., Amin, S., Bue, A. D., Cristani, M., & Galasso, F. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2949414 · Cited by 44 · see citing works
Neuromorphic Event-Based 3D Pose Estimation
Valeiras, D. R., Orchard, G., Ieng, S.-H., & Benosman, R. B. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00522 · Cited by 43 · see citing works
Li, B., He, M., Dai, Y., Cheng, X., & Chen, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5642-0 · Cited by 39 · see citing works
The need 4 speed in real-time dense visual tracking
Kowdle, A., Rhemann, C., Fanello, S., Tagliasacchi, A., Taylor, J., Davidson, P., Dou, M., Guo, K., Keskin, C., Khamis, S., et al. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275062 · Cited by 37 · see citing works
Animation of 3D Human Model Using Markerless Motion Capture Applied To Sports
Shingade, A., & Ghotkar, A. S. (2014) · International Journal of Computer Graphics & Animation · doi:10.5121/ijcga.2014.4103 · Cited by 34 · see citing works
Li, B., Chen, H., Chen, Y., Dai, Y., & He, M. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026283 · Cited by 28 · see citing works
Temporal Upsampling of Depth Maps Using a Hybrid Camera
Yuan, M., Gao, L., Fu, H., & Xia, S. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2812879 · Cited by 17 · see citing works
3D Human Pose Estimation with 2D Marginal HeatmapsPreprint
Nibali, A., He, Z., Morgan, S., & Prendergast, L. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01484 · Cited by 6 · see citing works
McREYNOLDS, T., & BLYTHE, D. (2005) · Elsevier eBooks · doi:10.1016/b978-155860659-3.50004-4 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- C. Brandli, R. Berner, M. Yang, S.-C. Liu, and T. Delbruck. A 240x180 130db 3µs latency global shutter spatiotempo- ral vision sensor. IEEE Journal of Solid-State Circuits, 49(10):2333-2341, 2014. 1
- Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. Smpl. ACM Transactions on Graphics, 34(6):1-16, 2015. 2
- Morgan Kaufmann, San Francisco, 2005. 4
- Weipeng Xu, Avishek Chatterjee, Michael Zollhöfer, Helge Rhodin, Dushyant Mehta, Hans-Peter Seidel, and Christian Theobalt. Monoperfcap. ACM Transactions on Graphics, 37(2):1-15, 2018. 2
Cited by
Sorted by citations
Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification
Ahmad, S., Scarpellini, G., Morerio, P., & Bue, A. D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00052 · Cited by 54 · see citing works
Self-citationMoveEnet: Online High-Frequency Human Pose Estimation with an Event Camera
Goyal, G., Pietro, F. D., Carissimi, N., Glover, A., & Bartolozzi, C. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00420 · Cited by 49 · see citing works
Efficient Human Pose Estimation via 3D Event Point Cloud
Chen, J., Shi, H., Ye, Y., Yang, K., Sun, L., & Wang, K. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00023 · Cited by 45 · see citing works
Neuromorphic Event-based Facial Expression Recognition
Berlincioni, L., Cultrera, L., Albisani, C., Cresti, L., Leonardo, A., Picchioni, S., Becattini, F., & Bimbo, A. D. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00432 · Cited by 41 · see citing works
Person Re-Identification without Identification via Event Anonymization
Ahmad, S., Morerio, P., & Del Bue, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01022 · Cited by 39 · see citing works
Self-citationA Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
Aydin, A., Gehrig, M., Gehrig, D., & Scaramuzza, D. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00579 · Cited by 38 · see citing works
Highly Efficient 3D Human Pose Tracking From Events With Spiking Spatiotemporal Transformer
Zou, S., Mu, Y., Ji, W., Wang, Z.-A., Zuo, X., Wang, S., Si, W., & Cheng, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3570056 · Cited by 17 · see citing works
Ahmad, S., Morerio, P., & Del Bue, A. (2024) · IEEE Access · doi:10.1109/access.2024.3399539 · Cited by 16 · see citing works
“Therefore, considering the advantages of event-camera over traditional cameras, several state-of-the-art methods [2], [3], [53], [54] have been proposed to solve 2D/3D human pose estimation in event-based vision.”
BackgroundMethodInfluentialSelf-citationEHoA: A Benchmark for Task-Oriented Hand-Object Action Recognition via Event Vision
Chen, W., Liu, S.-C., & Zhang, J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3393007 · Cited by 16 · see citing works
Towards a Dynamic Vision Sensor-based Insect Camera Trap
Gebauer, E., Thiele, S., Ouvrard, P., Sicard, A., & Risse, B. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00700 · Cited by 12 · see citing works
RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method
Yan, M., Zhang, Y., Cai, S., Fan, S., Lin, X., Dai, Y., Shen, S., Wen, C., Xu, L., Ma, Y., et al. (2024) · 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/cvpr52733.2024.00219 · Cited by 9 · see citing works
Adaptive Vision Transformer for Event-Based Human Pose Estimation
Yu, N., Ma, T., Zhang, J., Zhang, Y., Bao, Q., Wei, X., & Yang, X. S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681401 · Cited by 9 · see citing works
EventPointMesh: Human Mesh Recovery Solely From Event Point Clouds
Hori, R., Isogawa, M., Mikami, D., & Saitô, H. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3462816 · Cited by 7 · see citing works
Exploring event-based human pose estimation with 3D event representations
Yin, X., Shi, H., Chen, J., Wang, Z., Ye, Y., Wang, K., & Wang, K. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104189 · Cited by 5 · see citing works
Introducing A Framework for Single-Human Tracking Using Event-Based Cameras
Eisl, D., Herzog, F., Dugelay, J., Apvrille, L., & Rigoll, G. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222777 · Cited by 4 · see citing works
Continuous-Time Human Motion Field from Event Cameras
Wang, Z., Zhang, R., Liu, Z.-Y., Wang, Y., & Daniilidis, K. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01070 · Cited by 3 · see citing works
GraphEnet: Event-Driven Human Pose Estimation with a Graph Neural Network
Goyal, G., Thuong, P. C., Glover, A., Mizuno, M., & Bartolozzi, C. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00490 · Cited by 2 · see citing works
3D Human Scan With A Moving Event Camera
Kohyama, K., Shiba, S., & Aoki, Y. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00568 · Cited by 2 · see citing works
A Joint Global and Local Temporal Modeling for Human Pose Estimation with Event Cameras
Du, F., Shao, Z., Wang, X., Yang, J., & Dai, J. (2025) · Sensors · doi:10.3390/s25092868 · Cited by 1 · see citing works
Neuromophic High-Frequency 3d Dancing Pose Estimation in Dynamic EnvironmentPreprint
Zhang, Z., Chai, K., Yu, H., Majaj, R. M., Walsh, F., Wang, E., Mahbub, U., Siegelmann, H. T., Kim, D., & Rahman, T. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4353603
Zhao, X., Yang, L., Huang, W., Wang, Q., Wang, X., & Lou, Y. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01528-3
Segmentation of Human Body Parts using Growing Neural Gas with Event Camera
Eguchi, M., Obo, T., & Kubota, N. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650378
Event-Based Hand Detection on Neuromorphic Hardware Using a Sigma Delta Neural Network
Azzalini, L. J., Glüge, S., Struckmeier, J., & Sandamirskaya, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72359-9_26
Event-based Robust 3D Pose Estimation Using Time Series Data
Koizumi, K., & Watanabe, H. (2024) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic61668.2024.10731089
Measuring Cognitive Load Through Event Camera Based Human-Pose Estimation
Aitsam, M., Lacroix, D., Goyal, G., Bartolozzi, C., & Nuovo, A. G. D. (2025) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-81688-8_17
Chen, W., Kong, J., Jiang, M., & Tao, X. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023045
Zafar, M. H., Moosavi, S. K. R., & Sanfilippo, F. (2025) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3628064
Learning Local and Global Features from 3D Event Point Cloud for Human Pose Estimation
Du, F., Shao, Z., Wang, X., Yang, J., & Li, Y. (2025) · International Conference on Computer Graphics and Image Processing (CGIP) · doi:10.1109/cgip66596.2025.00014
Eseg: a Real-Time Human Instance Segmentation Model for Event-Based Cameras
Barry, M., & Widmer, A. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-30801-6_17
Human Mesh Reconstruction via Complementary Learning From Event Streams and Frames
Chen, L., Zhang, Z., & Wang, Y. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3714924
EventPoseFormer: Event-Based Real-time 3D Human Pose Estimation
Ali, M. O., Glover, A., Bartolozzi, C., Odone, F., & Goyal, G. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729448
- Authors
- Scarpellini, G., Morerio, P., & Del Bue, A.
- Affiliations
- Italian Institute of Technology (IT)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 1358-1368
- OpenAlex
- W3156301890
- Cited by
- 31
- References
- 68
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 13 · 2023: 5 · 2022: 2
- FWCI
- 1.64
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- 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 Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- 3D human pose estimation, event cameras, asynchronous events
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
