Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.01666 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914526513856512 |
|---|---|
| author | 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 |
| author_facet | 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 |
| 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01666 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction 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 Computer Vision and Pattern Recognition Artificial Intelligence Robotics 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. |
| title | IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2605.01666 |