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Cited by 182

The DOI matches its Crossref record; 178 of the 178 references it cites were found in a registry or an index.

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Grauman, Kristen, Westbury, Andrew, Torresani, Lorenzo, Kitani, Kris, Malik, Jitendra, Afouras, Triantafyllos, Ashutosh, Kumar, Baiyya, Vijay, Bansal, Siddhant, Boote, Bikram, Byrne, Eugene, Chavis, Zach, Chen, Joya, Cheng, Feng, Chu, Fu-Jen, Crane, Sean, Dasgupta, Avijit, Dong, Jing, Escobar, Maria, Forigua, Cristhian, Gebreselasie, Abrham, Haresh, Sanjay, Huang, Jing, Islam, Md Mohaiminul, Jain, Suyog, Khirodkar, Rawal, Kukreja, Devansh, Liang, Kevin J, Liu, Jia-Wei, Majumder, Sagnik, Mao, Yongsen, Martin, Miguel, Mavroudi, Effrosyni, Nagarajan, Tushar, Ragusa, Francesco, Ramakrishnan, Santhosh Kumar, Seminara, Luigi, Somayazulu, Arjun, Song, Yale, Su, Shan, Xue, Zihui, Zhang, Edward, Zhang, Jinxu, Castillo, Angela, Chen, Changan, Fu, Xinzhu, Furuta, Ryosuke, González, Cristina, Gupta, Prince, Hu, Jiabo, Huang, Yifei, Huang, Yiming, Khoo, Weslie, Kumar, Anush, Kuo, Robert, Lakhavani, Sach, Liu, Miao, Luo, Mi, Luo, Zhengyi, Meredith, Brighid, Miller, Austin, Oguntola, Oluwatumininu, Pan, Xiaqing, Peng, Penny, Pramanick, Shraman, Ramazanova, Merey, Ryan, Fiona, Shan, Wei, Somasundaram, Kiran, Song, Chenan, Southerland, Audrey, Tateno, Masatoshi, Wang, Huiyu, Wang, Yuchen, Yagi, Takuma, Yan, Mingfei, Yang, Xitong, Yu, Zecheng, Zha, Shengxin Cindy, Zhao, Chen, Zhao, Ziwei, Zhu, Zhifan, Zhuo, Jeff, Arbeláez, Pablo, Bertasius, Gedas, Damen, Dima, Engel, Jakob, Maria Farinella, Giovanni, Furnari, Antonino, Ghanem, Bernard, Hoffman, Judy, Jawahar, C. V., Newcombe, Richard, Park, Hyun Soo, Rehg, James M., Sato, Yoichi, Savva, Manolis, Shi, Jianbo, Shout, Mike Zheng, Wray, Michael·2024·2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr52733.2024.01834
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Abstract

We present Ego-Exo4D, a diverse, large-scale multi-modal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured ego-centric and exocentric video of skilled human activities (e.g., sports, music, dance, bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts, yielding long-form captures from 1 to 42 minutes each and 1,286 hours of video combined. The multimodal nature of the dataset is un-precedented: the video is accompanied by multichannel audio, eye gaze, 3D point clouds, camera poses, IMU, and multiple paired language descriptions-including a novel “expert commentary” done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity, we also present a suite of benchmark tasks and their annotations, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose. All resources are open sourced to fuel new research in the community.

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Research works printed but not linked: 34

  1. 10.1109/ICCV48922.2021.01532 · DOI
  2. 10.1007/978-3-030-58621-8_20 · DOI
  3. Contributors, 2020, Openmmlab pose estimation tool-box and benchmark. ht tps://gi thub. com/open-mmlab/mmpose
  4. 10.1007/s11263-021-01531-2 · DOI · Paper page
  5. 10.1109/CVPR.2019.00805 · DOI
  6. 10.2307/1129250 · DOI
  7. Girdhar, 2022, Omni-vore: A single model for many visual modalities, 16102
  8. Ho, 2020, Denoising dif-fusion probabilistic models, Advances in Neural Information Processing Systems, 33, 6840
  9. Howard, 2017, Mobilenets: Efficient convolutional neural networks for mobile vision applications.
  10. Kukelova, 2016, Ef-ficient intersection of three quadrics and applications in computer vision, 1799
  11. Lin, 2022, Egocen-tric video-language pretraining, NeurIPS
  12. Lin, 2017, Feature pyra-mid networks for object detection, 2117
  13. Liu, 2020, A 4.6 μm
  14. Liu, 2021, Towards unified sur-gical skill assessment, 9522
  15. Loshchilov, 2019, Decoupled weight de-cay regularization
  16. Mehta, 2021, Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer.
  17. Parmar, 2019, What and how well you performed? a multi task learning approach to action quality assessment, 304
  18. Polino, 2018, Model compression via distillation and quantization.
  19. Radford, 2023, Robust speech recognition via large-scale weak supervision, 28492
  20. Ramakrishnan, 2021, Habitat-matterport 3d dataset (HM3d):1000 large-scale 3d environments for embodied AI
  21. Rombach, 2021, Geometry-free view synthesis: Transformers and no 3d pri-ors, 14356
  22. Sener, 2022, As-semblyl0l: A large-scale multi-view video dataset for understanding procedural activities, 21096
  23. Song, 2023, Ego4d goal-step: Toward hierarchical understanding of procedural activities, NeurIPS
  24. 10.48550/ARXIV.1212.0402 · DOI · Paper page
  25. Soran, 2015, Generating notifications for missing actions: Donot forget to turn the lights off!, ICCV, 4669
  26. Mukund Varma, 2023, Is attention all that neRF needs?
  27. Tschernezki, 2023, EPIC Fields: Marrying 3D Geometry and Video Understanding
  28. Wen, 2021, Seeing the un-seen: Predicting the first-person camera wearer&s location and pose in third-person scenes, 3446
  29. Wong, 2022, As-sistq: Affordance-centric question-driven task completion for egocentric assistant
  30. Xu, 2021, Long short-term transformer for online action detection, Advances in Neural Information Processing Systems, 34, 1086
  31. Yuan, 2019, Ego-pose estimation and fore-casting as real-time pd control
  32. 10.1007/978-3-031-19772-7_29 · DOI · Paper page
  33. Zhao, 2022, P3iv: Prob-abilistic procedure planning from instructional videos with weak supervision, 2938
  34. 10.1109/CVPR52729.2023.01033 · DOI

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Metadata

Authors
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., Byrne, E., Chavis, Z., Chen, J., Cheng, F., Chu, F.-J., Crane, S., Dasgupta, A., Dong, J., Escobar, M., Forigua, C., Gebreselasie, A., Haresh, S., Huang, J., Islam, M. M., Jain, S., Khirodkar, R., Kukreja, D., Liang, K. J., Liu, J.-W., Majumder, S., Mao, Y., Martin, M., Mavroudi, E., Nagarajan, T., Ragusa, F., Ramakrishnan, S. K., Seminara, L., Somayazulu, A., Song, Y., Su, S., Xue, Z., Zhang, E., Zhang, J., Castillo, A., Chen, C., Fu, X., Furuta, R., González, C., Gupta, P., Hu, J., Huang, Y., Huang, Y., Khoo, W., Kumar, A., Kuo, R., Lakhavani, S., Liu, M., Luo, M., Luo, Z., Meredith, B., Miller, A., Oguntola, O., Pan, X., Peng, P., Pramanick, S., Ramazanova, M., Ryan, F., Shan, W., Somasundaram, K., Song, C., Southerland, A., Tateno, M., Wang, H., Wang, Y., Yagi, T., Yan, M., Yang, X., Yu, Z., Zha, S. C., Zhao, C., Zhao, Z., Zhu, Z., Zhuo, J., Arbeláez, P., Bertasius, G., Damen, D., Engel, J., Maria Farinella, G., Furnari, A., Ghanem, B., Hoffman, J., Jawahar, C. V., Newcombe, R., Park, H. S., Rehg, J. M., Sato, Y., Savva, M., Shi, J., Shout, M. Z., & Wray, M.
Affiliations
  • FAIR, Meta
  • Project Aria, Meta
  • University of Bristol
  • University of Illinois,Urbana Champaign
  • University of Minnesota
  • National University of Singapore
  • Carnegie Mellon University
  • International Institute of Information Technology,Hyderabad
  • Universidad de los Andes
  • Simon Fraser University
  • University of North Carolina,Chapel Hill
  • University of Catania
  • University of Texas at Austin
  • University of Pennsylvania
  • University of Tokyo
  • Meta
  • Indiana University
  • Johns Hopkins University
  • King Abdullah University of Science and Technology
  • Georgia Tech
Venue
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2024-06-16
Pages
19383-19400
DOI
10.1109/cvpr52733.2024.01834
Cited by
182
References
178
Licence
Funders
NSF (CNS-2308994,DRL-2112635)

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Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., Byrne, E., Chavis, Z., Chen, J., Cheng, F., Chu, F.-J., Crane, S., Dasgupta, A., Dong, J., Escobar, M., . . . Wray, M. (2024). Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52733.2024.01834

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