HUI360-Videos

HUI360: A 360° Egocentric Dataset and Baselines for Human-Robot Interaction Anticipation (IEEE FG 2026)

Video data for HUI360, a large-scale 360° egocentric dataset for human-robot interaction anticipation in the wild.

Related resources

Resource	Link
Project website	hucebot.github.io/hui360
Paper (HAL)	hal.science/view/index/docid/5609928
Annotation & processing pipeline	Interact360
Skeleton annotations (open)	rlorlou/HUI360
SSUP processed videos (gated)	rlorlou/HUI360-Videos-SSUP
SSUP original dataset	IRL-CT/SSUP-HRI

Annotation tool. Annotations in this repository were produced with the Interact360 pipeline (automatic detection, tracking, pose estimation, and interaction labelling) and manually refined with its visualize.py GUI. Install Interact360 with the [gui] extra to inspect or correct annotations. Fields such as validity and engagement_level may reflect these manual corrections.

Dataset sources

This dataset is composed of two sources whose access rules are different:

• Recordings made with the Shelfy Robot at INRIA Nancy in July and October 2025
• Recordings made by the Interaction Research Lab team of Cornell University in New York in October 2022 and July 2023, reprocessed to provide a unified formatting (SSUP — F. Bu, W. Ju, 2024)

The annotations are freely available and contain the 2D human pose data used to create HUI360 on Hugging Face.

Structure and content

Access rules

The full dataset is gated on Hugging Face and access can only be granted under the following terms:

• Images recorded by the Shelfy Robot at INRIA are available on request for research purposes only. To request access to the raw videos:

1. Download and fill the appropriate DTA for your country of affiliation:
2. DTA — EEA and adequate countries or DTA — outside the EEA / non-adequate countries

As of October 16, 2023, institutes located in the following territories may use the EEA / adequate-countries form: countries of the European Union, Iceland, Liechtenstein, Norway, Andorra, Argentina, Canada, Faroe Islands, Guernsey, Israel, Isle of Man, Japan, Jersey, New Zealand, Switzerland, Uruguay, South Korea, and the United Kingdom. All other affiliations should use the outside-EEA form.

3. Sign it using your academic / institutional email address.
4. Send the completed form to huidataset@inria.fr.
5. Wait for approval from our legal team.

In the meantime, register on Hugging Face with the same academic email and request access to HUI360-Videos. Access will be granted once the DTA is approved.

• Images recorded by Cornell's IRL team as part of SSUP are subject to their own agreement (available at IRL-CT/SSUP-HRI). As per clause 5, the data cannot be redistributed to users who have not been approved for access to the original dataset. Either request access to the original dataset and split it as specified in (1) below, or — if granted access by the SSUP authors — request access to our processed videos at rlorlou/HUI360-Videos-SSUP after sending us proof of access.

Note

The dataset is structured in 99 recordings (29 from SSUP Dataset and 70 from Shelfy), each recordings is subdivided in episodes.

(1) In SSUP (XXX_astor_place_XXX or XXX_albee_square_XXX recordings) data extraction the episodes are a simple subdvidision of the original videos every 5 minutes (except for 2022_09_21_astor_place_recycle that was accidentally divided in 2 minutes sequences) (2) In Shelfy recordings (rosbag2_XXX recordings) the episodes have a variable length : they start 10 seconds before someone is detected and end after 10 second without detection (detections were performed with another camera that was only front facing, so people in the back or on the sides are not considered for the episode splitting)

Overall structure (after merging)

• Unzip (if necessary) and merge video_full_shelfy and video_full_ssup directories into video_full
• Unzip (if necessary) and merge video_mini_shelfy and video_mini_ssup directories into video_mini

The structure is separated for easier downloading :

HUI360-Video/
├── README.md
├── annotations
│   ├── 2022_09_21_astor_place_landfill
│   ...
│   └── rosbag2_2025_10_20-16_02_46
├── dataset
│   ├── 2022_09_21_astor_place_landfill
│   ...
│   └── rosbag2_2025_10_20-16_02_46
├── video_full
│   ├── 2022_09_21_astor_place_landfill
│   ...
│   └── rosbag2_2025_10_20-16_02_46
└── video_mini
    ├── 2022_09_21_astor_place_landfill
    ...
    └── rosbag2_2025_10_20-16_02_46

In details :

HUI360-Video/
├── README.md
├── annotations
│   ├── 2022_09_21_astor_place_landfill
│   │   └── episodes
│   │       ├── 0000
│   │       │   ├── annotations_360.json
│   │       │   ├── images_360_filenames.txt
│   │       │   └── interaction_zone_tracking_360.json
|   |       ...  
│   │       └── 0012
│   │           ├── ...
│   │           └── ...
│   ...
│   └── rosbag2_2025_10_20-16_02_46
│       └── episodes
│           ├── 0000
│           │   ├── annotations_360.json
│           │   └── images_360_filenames.txt
|           ...  
│           └── 0034
│               ├── ...
│               └── ...
├── dataset
│   ├── 2022_09_21_astor_place_landfill
│   │   ├── episodes
│   │   │   ├── 0000
│   │   │   │   └── images_360
│   │   │   │       ├── ...
│   │   │   │       ├── ...
│   │   │   │       ├── ...
│   │   │   │       └── ...
|   |   |   ...  
│   │   │   └── 0012
│   │   │       └── ...
|   │   ├── episodes_flag.json
│   │   └── episodes.json
│   ...
│   └── rosbag2_2025_10_20-16_02_46
│       ├── episodes
│       ...
|       ├── episodes_flag.json
│       └── episodes.json
├── video_full
│   ├── 2022_09_21_astor_place_landfill
│   │   └── episodes
│   │       ├── 0000
│   │       │   └── images_360_full.mp4
|   |       ...  
│   │       └── 0012
│   │           ├── ...
│   │           └── ...
│   ...
│   └── rosbag2_2025_10_20-16_02_46
│       └── episodes
│           ├── ...
│           └── ...
└── video_mini
    ├── 2022_09_21_astor_place_landfill
    │   └── episodes
    │       ├── 0000
    │       │   └── images_360_mini.mp4
    |       ...  
    │       └── 0012
    │           ├── ...
    │           └── ...
    ...
    └── rosbag2_2025_10_20-16_02_46
        └── episodes
            ├── ...
            └── ...

Recording level annotation files

The files episodes.json and episodes_flag.json contains informations related to each episode in the recording, the first one is just for the name and timestamp and was created at the extraction phase, it is redundant with the second one that contains specific flags for each episode, anything other than valid,checked should not be used. Notable flags are nov (episode discarded because of abnormal behaviors attributed to novelty effect), and GDPR episode removed because someone requested their data to be removed in compliance with GDPR policy.

• episodes.json

[
    {
        "session": "2022_09_21_astor_place_landfill",
        "episode": "0000",
        "start_time": 1763542047.679756,
        "end_time": 1763542064.8130896
    },
    {
        "session": "2022_09_21_astor_place_landfill",
        "episode": "0001",
        "start_time": ...,
        "end_time": ...
    },
    ...
]

• episodes_flag.json

{
    "0000": {
        "start_time": 1756981067.0,
        "end_time": 1756981366.9999995,
        "start_time_formatted": "2025-09-04 12:17:47",
        "end_time_formatted": "2025-09-04 12:22:47",
        "flag": "valid,checked"
    },
    "0001": {
        "start_time": 1756981367.0,
        "end_time": 1756981666.9999995,
        "start_time_formatted": "2025-09-04 12:22:47",
        "end_time_formatted": "2025-09-04 12:27:47",
        "flag": "valid,checked"
    },
    ...
}

Episode level annotation and data files

Data

• images_360 are empty directories that should normally contain the raw and high resolution panoramic images as .jpeg, they are not kept to save space, please refer to the last section for informations on how to get them (either from the original full quality data or extract them from the videos)
• images_360_full.mp4 are compressed videos at full resolution (3840*1920) but whose quality is slightly lower than original JPEGs
• images_360_mini.mp4 are compressed videos at reduced resolution (960*480) for very fast downlooading / loading

All the data were recorded or subsampled at 15 fps

Annotations

• interaction_zone_tracking_360.json are used for SSUP where the "interaction zone" (the trash can) is mobile, the structure for the detections and masks is the same as annotations_360.json but with only one detection per image

• annotations_360.json is a list of dictionnaries (one per frame, ordered as in the videos) that contains metadata about the frame and a list of detections (with associated track_id) in the frame, these detections are themselves structured a individual dictionnaries, with all the data regarding the track at the given frame (keypoints, bounding box, segmentation mask encoded as RLE).

  In each detection you will find the following keys :

  • "track_id" is assigned to each person tracked along the episode.
  • The bounding box coordinates are expressed as xmin, ymin, xmax and ymax in pixels. As the 360 image is wrapped, some may exceed the image size (but should not be negative) : pixel (4283,783) corresponds to pixel (443,783).
  • "validity" flag is assigned (default is valid) but it might be corrected in the Interact360 manual correction tool (visualize.py)
  • "mask_size" indicates the total number of pixels in the mask
  • "mask_rle" is the RLE encoding of the mask as a string of comma separated values, it can be decoded using decode_RLE from misc.rle_tools.py (using torch tensor format) : mask = decode_RLE(torch.from_numpy(np.array(det["mask_rle"].split(",")).astype(np.int32)).to(DEVICE), shape = (width, height))
  • "engagement_level" is automatically determined by the interaction detection process and can be corrected from the Interact360 manual correction tool (visualize.py). 0 corresponds to no engagement, 3 corresponds to interaction (currently happening).
  • "intersection_pixels" is the number of pixels intersecting between the person mask and the interesection zone mask (either fixed or mobile). NB : in older versions this was not exported so "intersection_pixels" : -1 indicates that this information was not saved.
  • "scores_sapiens_308_03b" or "scores_sapiens_308_06b" is a list of float with lenght 308 that are the confidence scores associated with each keypoint of "keypoints_sapiens_308_03b"
  • "scores_vitpose" is a list of float with lenght 17 that are the confidence scores associated with each keypoint of "keypoints_vitpose"
  • "keypoints_sapiens_308_03b" or "keypoints_sapiens_308_06b" are the keypoints coordinates (in pixels) with the Goliath format of Sapiens, some were extracted using the 03b pose model and others with 06b pose model.
  • "keypoints_vitpose" are the keypoints coordinates (in pixels) with the COCO-17 format of ViTPose. The keypoints are expressed as lists of length 308*2 or 17*2 (alternating x and y coordinates), note that they are expressed as the pixel position of the joint in the image, so when the box is wrapped around they may end up not being in the box numerically, or showing two parts of the body at the two ends of the image. If a keypoint_XXX key is not present it means that these keypoints were not extracted, probably because the track was already labelled as invalid or the box is too small.

Overall structure of annotations_360.json

[
  {
    "image_file": "1970_01_01_02_00_00_000000.jpg",
    "image_shape": [
        1920,
        3840,
        3
    ]
    "detections": [
      {
        "track_id": 0,
        "xmin": 4283,
        "ymin": 783,
        "xmax": 4451,
        "ymax": 1220,
        "validity": "valid",
        "mask_size": 44302,
        "mask_rle": "0,3007221,17,3820,...,13,2687454",
        "engagement_level": 3,
        "intersection_pixels": 18545,
        "keypoints_vitpose": [
            481,
            812,
            ...
        ],
        "scores_vitpose": [
            0.53,
            ...
        ],
        "keypoints_sapiens_308_03b": [
            498,
            816,
            ...
        ],
        "scores_sapiens_308_03b": [
            0.45,
            ...
        ]
      },
      {
        "track_id": 4,
        "xmin": 1253,
        "ymin": 721,
        "xmax": 1611,
        "ymax": 1520,
        "validity": "invalid",
        "mask_size": 145304,
        "mask_rle": "0,8494527,21,4244,...,14,1425546",
        "engagement_level": 0,
        "intersection_pixels": 0,
        "keypoints_vitpose": [
            1481,
            812,
            ...
        ],
        "scores_vitpose": [
            0.43,
            ...
        ],
        "keypoints_sapiens_308_06b": [
            1498,
            816,
            ...
        ],
        "scores_sapiens_308_06b": [
            0.64,
            ...
        ]
      },
    ]
  },
  {
    "image_file": "1970_01_01_02_00_00_066667.jpg",
    "image_shape": [
        1920,
        3840,
        3
    ]
    "detections": [
      ...
    ]
  },
  ...
]

Citation

If you use this dataset, please cite:

@INPROCEEDINGS{11556969,
  author={Lorenzo-Louis, Raphael and Amadio, Fabio and Luvison, Bertrand and Ivaldi, Serena},
  booktitle={2026 IEEE 20th International Conference on Automatic Face and Gesture Recognition (FG)},
  title={HUI360 : A 360° Egocentric Dataset and Baselines for Human-Robot Interaction Anticipation},
  year={2026},
  volume={},
  number={},
  pages={1-9},
  doi={10.1109/FG67764.2026.11556969}
}

If you use the SSUP recordings included in this dataset, please also cite the original SSUP-HRI dataset:

@inproceedings{BuJ24a,
  author={Bu, Fanjun and Ju, Wendy},
  title={SSUP-HRI: Social Signaling in Urban Public Human-Robot Interaction Dataset},
  booktitle={Social Robotics -- 16th International Conference, ICSR + AI 2024, Odense, Denmark, October 23--26, 2024, Proceedings, Part III},
  series={Lecture Notes in Computer Science},
  volume={15563},
  pages={479--487},
  publisher={Springer},
  year={2024},
  doi={10.1007/978-981-96-3525-2_40}
}