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Bibliographic Details
Main Authors:	Zhang, Haoshen, Wen, Di, Peng, Kunyu, Schneider, David, Zhong, Zeyun, Jaus, Alexander, Marinov, Zdravko, Wei, Jiale, Liu, Ruiping, Zheng, Junwei, Chen, Yufan, Zhang, Yufeng, Luo, Yuanhao, Qi, Lei, Stiefelhagen, Rainer
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence Robotics
Online Access:	https://arxiv.org/abs/2605.01666
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Table of Contents:

We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object Interactions (HOI), motivated by the need for high-quality structured supervision for learning robot manipulation from human demonstration. IMPACT-HOI frames this task as the incremental resolution of a partially specified, onset-anchored event state. A trust-calibrated controller selects among direct queries, human-confirmed suggestions, and conservative completions based on empirical annotator behavior and evidence quality. A risk-bounded execution protocol, utilizing atomic rollback, ensures that human-confirmed decisions are preserved against conflicting automated updates. A user study with 9 participants shows a 13.5% reduction in manual annotation actions, a 46.67% event match rate, and zero confirmed-field violations under the studied protocol. The code will be made publicly available at https://github.com/541741106/IMPACT_HOI.

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