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EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World

24 Mar 2024CVPR 2024 1arXiv:2403.16182archive 2025-07-28

Yifei HUANG, Guo Chen, Jilan Xu, Mingfang Zhang, Lijin Yang, Baoqi Pei, Hongjie Zhang, Lu Dong, Yali Wang, LiMin Wang, Yu Qiao

Being able to map the activities of others into one's own point of view is one fundamental human skill even from a very early age. Taking a step toward understanding this human ability, we introduce EgoExoLearn, a large-scale dataset that emulates the human demonstration following process, in which individuals record egocentric videos as they execute tasks guided by demonstration videos. Focusing on the potential applications in daily assistance and professional support, EgoExoLearn contains egocentric and demonstration video data spanning 120 hours captured in daily life scenarios and specialized laboratories. Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints. To this end, we present benchmarks such as cross-view association, cross-view action planning, and cross-view referenced skill assessment, along with detailed analysis. We expect EgoExoLearn can serve as an important resource for bridging the actions across views, thus paving the way for creating AI agents capable of seamlessly learning by observing humans in the real world. Code and data can be found at: https://github.com/OpenGVLab/EgoExoLearn

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attentive_entropy OpenGVLab/EgoExoLearn/action_anticipation_planning_benchmark/loss.py official repository ran fingerprinted MIT (permissive) · 5dafc748f41191f5 · report
attentive_entropy OpenGVLab/EgoExoLearn/temporal_action_segmentation_benchmark/loss.py official repository ran fingerprinted MIT (permissive) · 829b3a3e9d894fa8 · report
binary_precision_recall_curve OpenGVLab/EgoExoLearn/action_anticipation_planning_benchmark/anticipation_main.py official repository ran MIT (permissive) · b25ea1a523927d43 · report
cross_entropy_soft OpenGVLab/EgoExoLearn/action_anticipation_planning_benchmark/loss.py official repository ran fingerprinted MIT (permissive) · b1e8dce608f3f658 · report
dis_MCD OpenGVLab/EgoExoLearn/action_anticipation_planning_benchmark/loss.py official repository ran fingerprinted MIT (permissive) · 0c3c2aa9cf2ac2f0 · report
dis_mcd OpenGVLab/EgoExoLearn/temporal_action_segmentation_benchmark/loss.py official repository ran fingerprinted MIT (permissive) · e1dc22b6c3aff0f2 · report
mean_average_precision OpenGVLab/EgoExoLearn/action_anticipation_planning_benchmark/anticipation_main.py official repository ran MIT (permissive) · 2b38a33ad005fe8c · report
train OpenGVLab/EgoExoLearn/action_anticipation_planning_benchmark/anticipation_main.py official repository unverified MIT (permissive) · 215d8bffab54d8ff · report

Tasks

Action AnticipationAction Quality AssessmentLong Term AnticipationVideo Retrieval

Datasets

Introduced by this paper, per the archive.

EgoExoLearn

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Anticipation EgoExoLearn Action anticipation baseline (co-training, with gaze) Accuracy 45.45 #1 of 2 Archive leaderboard report
Action Anticipation EgoExoLearn Action anticipation baseline (co-training, no gaze) Accuracy 38.7 #2 of 2 Archive leaderboard report
Action Quality Assessment EgoExoLearn RAAN+TL+Gaze Accuracy 81.27 #1 of 2 Archive leaderboard report
Action Quality Assessment EgoExoLearn RAAN+TL Accuracy 79.875 #2 of 2 Archive leaderboard report
Video Retrieval EgoExoLearn cross-view association baseline (gaze, val) Accuracy 48.35 #1 of 2 Archive leaderboard report
Video Retrieval EgoExoLearn cross-view association baseline (no gaze, val) Accuracy 44.15 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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