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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.16797 |
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| _version_ | 1866916018191859712 |
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| author | Yu, Liuchuan Murat, Erdem Wang, Beichen Zeng, Yan Luo, Tingting Zhou, Huizhen Li, Shanghao Feng, Huining Zhao, Zhigen Yang, Ning Jing, Ke Liu, Yunhao Sheng, Ruoya |
| author_facet | Yu, Liuchuan Murat, Erdem Wang, Beichen Zeng, Yan Luo, Tingting Zhou, Huizhen Li, Shanghao Feng, Huining Zhao, Zhigen Yang, Ning Jing, Ke Liu, Yunhao Sheng, Ruoya |
| contents | Egocentric video is increasingly used as a data source for robot learning, activity understanding, and embodied AI research, but collecting it at scale remains fragmented in practice: each candidate host device, such as an Android phone, iPhone, iPad, smart glasses, or extended reality (XR) headset, exposes a different SDK, a different policy on raw camera access, and different limitations on external USB cameras and on-device tracking. Synchronized ego-view and wrist-view capture is therefore typically obtained by either committing to a single proprietary platform or building one-off rigs that do not transfer across devices. To address this gap, we present EgoKit, a toolkit that exposes the same egocentric recording workflow across six heterogeneous host devices. Across all supported devices, EgoKit presents the same recording interaction and produces locally stored video with a uniform log format; on XR headsets, it additionally logs head pose and OpenXR-standard 26-joint hand tracking aligned to the video streams. The companion accessories, including two wrist cameras with mounts, a head strap, and a USB-C hub, add wrist-view capture to any supported host without custom hardware fabrication. EgoKit is available at \url{https://egokit.chuange.org/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16797 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices Yu, Liuchuan Murat, Erdem Wang, Beichen Zeng, Yan Luo, Tingting Zhou, Huizhen Li, Shanghao Feng, Huining Zhao, Zhigen Yang, Ning Jing, Ke Liu, Yunhao Sheng, Ruoya Computer Vision and Pattern Recognition Robotics Egocentric video is increasingly used as a data source for robot learning, activity understanding, and embodied AI research, but collecting it at scale remains fragmented in practice: each candidate host device, such as an Android phone, iPhone, iPad, smart glasses, or extended reality (XR) headset, exposes a different SDK, a different policy on raw camera access, and different limitations on external USB cameras and on-device tracking. Synchronized ego-view and wrist-view capture is therefore typically obtained by either committing to a single proprietary platform or building one-off rigs that do not transfer across devices. To address this gap, we present EgoKit, a toolkit that exposes the same egocentric recording workflow across six heterogeneous host devices. Across all supported devices, EgoKit presents the same recording interaction and produces locally stored video with a uniform log format; on XR headsets, it additionally logs head pose and OpenXR-standard 26-joint hand tracking aligned to the video streams. The companion accessories, including two wrist cameras with mounts, a head strap, and a USB-C hub, add wrist-view capture to any supported host without custom hardware fabrication. EgoKit is available at \url{https://egokit.chuange.org/}. |
| title | EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2605.16797 |