Prince Gupta

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4ranked-venue papers

2as first author

2since 2021 · last 2025

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 3 papers		Video understanding and tracking · 52% Face, body and person analysis · 35% 3D vision · 7%
Computer graphics and multimedia 2 papers		Multimedia analysis and retrieval · 51% Virtual and augmented reality · 49%

Topics — the 8 heaviest of 10, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Computer vision › Face, body and person analysis › human pose estimation › 3d pose estimation 3d hand and body pose estimation	1.6	2	2025	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives · Int. J. Comput. Vis. 2025 Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives · CVPR 2024
Computer vision › Video understanding and tracking egocentric video understanding	1.6	2	2025	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives · Int. J. Comput. Vis. 2025 Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives · CVPR 2024
Computer vision › Video understanding and tracking activity understanding	0.8	1	2024	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives · CVPR 2024
Computer vision › 3D vision › 3d reconstruction multi-view reconstruction	0.3	1	2025	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives · Int. J. Comput. Vis. 2025
Virtual and augmented reality immersive interaction	0.1	1	2010	Markerless tracking using Polar Correlation of camera optical flow · VR 2010
Virtual and augmented reality › tracking markerless tracking	0.1	1	2010	Markerless tracking using Polar Correlation of camera optical flow · VR 2010
Computer vision › 3D vision camera pose estimation	0.0	1	2010	Markerless tracking using Polar Correlation of camera optical flow · VR 2010
Computer vision › 3D vision › motion estimation optical flow	0.0	1	2010	Markerless tracking using Polar Correlation of camera optical flow · VR 2010

Methods — techniques the papers use, named apart from their topics

multimodal dataset · 0.9benchmark tasks · 0.9polar correlation · 0.2

Year	Publication	Venue	Position
2025	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives abstract We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric 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 unprecedented: 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. https://ego-exo4d-data.org/ Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Makoto Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zachary Chavis, Joya Chen, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong 0002, María Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang 0020, Md Mohaiminul Islam, Suyog Dutt Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J. Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh K. Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina González, Prince Gupta, Jiabo Hu, Yifei Huang 0002, Yiming Huang 0011, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu 0007, Mi Luo, Zhengyi Luo 0002, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Kiran K. Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao 0002, Ziwei Zhao 0003, Zhifan Zhu 0001, Jeff Zhuo, Pablo Andrés Arbeláez, Gedas Bertasius, David Crandall, Dima Damen, Jakob J. Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard A. Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Manolis Savva, Jianbo Shi, Mike Zheng Shout, Michael Wray	Int. J. Comput. Vis.	49
2024	Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives 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. Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Makoto Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zachary Chavis, Joya Chen, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong 0002, María Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang 0020, Md Mohaiminul Islam, Suyog Dutt Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J. Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh K. Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina González, Prince Gupta, Jiabo Hu, Yifei Huang 0002, Yiming Huang 0011, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu 0007, Mi Luo, Zhengyi Luo 0002, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Kiran K. Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao 0002, Ziwei Zhao 0003, Zhifan Zhu 0001, Jeff Zhuo, Pablo Andrés Arbeláez, Gedas Bertasius, Dima Damen, Jakob J. Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard A. Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Manolis Savva, Jianbo Shi, Mike Zheng Shout, Michael Wray	CVPR	49
2013	Markerless tracking and gesture recognition using polar correlation of camera optical flow Prince Gupta, Niels da Vitoria Lobo, Joseph J. LaViola Jr.	Mach. Vis. Appl.	1
2010	Markerless tracking using Polar Correlation of camera optical flow abstract We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm. Experiments comparing our device with an electromagnetic tracker show that its average tracking accuracy is 80% over 185 frames, and it is able to track large range motions even in outdoor settings. Prince Gupta, Niels da Vitoria Lobo, Joseph J. LaViola Jr.	VR	1