| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pretty_name: STOU |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - visual-question-answering |
| tags: |
| - safety |
| - egocentric |
| - video |
| - audio |
| - multimodal |
| viewer: false |
| extra_gated_heading: "Access STOU" |
| extra_gated_prompt: >- |
| STOU contains harmful user requests written for AI safety evaluation. |
| By requesting access you agree to use it only for non-commercial research on |
| the safety of AI systems, not to use it to cause harm, and to obtain the |
| seed videos (Ego4D, Aria Everyday Activities, Nymeria+) yourself under |
| their own licenses. |
| extra_gated_fields: |
| Name: text |
| Affiliation: text |
| I agree to use this dataset only for non-commercial safety research: checkbox |
| --- |
| |
| # STOU |
|
|
| STOU is a benchmark for evaluating the safety of multimodal assistants on egocentric (first-person) video. Each instance pairs a short egocentric video clip with a harmful user request, given as text and as synthesized speech. |
|
|
| This release contains **1,952 instances** built on **713 video clips**. The clips come from three seed datasets: Ego4D, Aria Everyday Activities (AEA), and Nymeria+. |
|
|
| > **Videos are not redistributed.** The licenses of the seed datasets do not allow it. Download the seed datasets yourself and run `reproduce_clips.py` to recreate every clip from the timestamps in `clip_timestamps.csv`. |
|
|
| ## Contents |
|
|
| ``` |
| STOU/ |
| ├── README.md |
| ├── clip_timestamps.csv # source video + start/end time of each clip (see note below) |
| ├── reproduce_clips.py # recreates the clips from the seed datasets |
| └── <HARM_TARGET>/<HARM_TYPE>/ |
| ├── STOU.json # instances of this harm target × harm type |
| ├── gold_annotations.json # reference action / harm annotations for evaluation |
| └── audio/marin/<instance_id>.wav # synthesized speech of the text query |
| ``` |
|
|
| There are three harm targets and six harm types, for 18 categories in total. |
|
|
| | Harm target | # | Harm type | # | |
| |---|---:|---|---:| |
| | `SELF` | 712 | `FINANCIAL` | 337 | |
| | `OTHERS` | 758 | `PHYSICAL` | 449 | |
| | `UNSPECIFIED` | 482 | `PRIVACY` | 198 | |
| | | | `PSYCHOLOGICAL` | 345 | |
| | | | `SEXUAL` | 282 | |
| | | | `SOCIETAL` | 341 | |
|
|
| | Seed dataset | Clips | Instances | |
| |---|---:|---:| |
| | Ego4D | 485 | 1,454 | |
| | Aria Everyday Activities | 128 | 313 | |
| | Nymeria+ | 100 | 185 | |
|
|
| `clip_timestamps.csv` lists 872 clips. 713 of them are used by the instances in this release, and the other 159 are not referenced. |
|
|
| ## Instance format |
|
|
| Each `STOU.json` maps an instance id to a record with these fields: |
|
|
| | Field | Description | |
| |---|---| |
| | `video_name` | Same as `clip_file`. | |
| | `source_dataset` | `ego4d`, `aea`, or `nymeria_plus`. | |
| | `clip_file` | The clip's file name. It is the key into `clip_timestamps.csv`. | |
| | `video_path` | Where `reproduce_clips.py --output-dir clips` writes the clip, relative to the release root. | |
| | `video_description` | Model-generated description of the clip that was used during query generation. It is `null` for AEA clips (not released, per the AEA license) and for instances created without a generated description. | |
| | `text-query` | The harmful user request (text). | |
| | `audio` | Speech version of `text-query` (24 kHz mono WAV). The path is relative to the release root. | |
|
|
| `gold_annotations.json` in the same folder uses the same instance ids. It holds the reference annotations used for evaluation: |
|
|
| | Field | Description | |
| |---|---| |
| | `axis1`, `axis2` | Harm target and harm type. | |
| | `text_query` | Same as `text-query` above. | |
| | `gold_action` | `intended_action`: the action the user is asking for. | |
| | `gold_harm` | `main_harm` (`potential_target`, `harm_mechanism`, `consequence`) and `acceptable_alternate_harms`. | |
| | `gold_annotation_status` | Annotation status (`ok` for all released instances). | |
| | `video_name`, `source_dataset`, `clip_file`, `video_path` | Same as in `STOU.json`. | |
|
|
| ## Reproducing the video clips |
|
|
| ### 1. Get the seed datasets |
|
|
| Accept each dataset's license and download it from the official source. |
|
|
| | Dataset | What to download | Expected layout (`--*-dir`) | |
| |---|---|---| |
| | [Ego4D](https://ego4d-data.org/) | v2 `full_scale` videos | `<dir>/<video_uid>.mp4` | |
| | [Aria Everyday Activities](https://www.projectaria.com/datasets/aea/) | release 1.0.0, `main_vrs` | `<dir>/<sequence>/recording.vrs` | |
| | [Nymeria+](https://www.projectaria.com/datasets/nymeria/) | head recording VRS | `<dir>/<sequence>/recording_head/data/data.vrs` | |
|
|
| The `aria_dataset_downloader` from `projectaria_tools` creates the AEA layout above. |
|
|
| ### 2. Run the script |
|
|
| ```bash |
| pip install projectaria-tools==2.2.0 moviepy==2.2.1 numpy tqdm # ffmpeg must be on PATH |
| |
| python reproduce_clips.py \ |
| --timestamps clip_timestamps.csv \ |
| --output-dir clips \ |
| --ego4d-dir /path/to/ego4d/v2/full_scale \ |
| --aea-dir /path/to/aea \ |
| --nymeria-dir /path/to/nymeria_plus \ |
| --num-workers 8 |
| ``` |
|
|
| The script writes clips to `clips/<source_dataset>/<clip_file>`, which matches `video_path` in the JSON files. You can omit a `--*-dir` flag to skip that dataset. |
|
|
| ### Timestamp format (`clip_timestamps.csv`) |
| |
| | Column | Description | |
| |---|---| |
| | `source_dataset`, `source_id` | Seed dataset, and its Ego4D `video_uid` or Aria sequence name. | |
| | `clip_file` | Clip file name, as referenced by the JSON files. | |
| | `start_sec`, `end_sec`, `duration_sec` | Clip window in seconds. | |
| | `time_reference` | What `start_sec` is measured from. For Ego4D this is the start of the `full_scale` video. For AEA and Nymeria+ it is the first audio record (stream `231-1`, RECORD_TIME), which is the origin used by `projectaria_tools`' `Vrs2Mp4Converter`. | |
| | `time_origin_ns`, `clip_start_device_ns`, `clip_end_device_ns` | AEA / Nymeria+ only: the same window as absolute device timestamps (ns). | |
|
|
| ## Licenses and attribution |
|
|
| The seed videos remain under their original licenses. You must obtain them yourself and comply with each license. Use is limited to non-commercial research. |
|
|
| - **Ego4D**: [Ego4D License Agreement](https://ego4d-data.org/). Please cite: "Egocentric Live 4D Perception (Ego4D) Database: A large-scale first-person video database, supporting research in multi-modal machine perception for daily life activity. Ego4D Consortium 2020. https://sites.google.com/view/ego4d/home". |
| - **Aria Everyday Activities**: [AEA Dataset License Agreement](https://www.projectaria.com/datasets/aea/license/) (non-commercial research only). Attribution: "Aria Everyday Activities Dataset, Meta Reality Labs-R". |
| - **Nymeria+**: non-audio data under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/); audio data under the Meta Non-Commercial Research License. Copyright (c) Meta Platforms, Inc. and affiliates. |
|
|
| The STOU text queries, labels, gold annotations, and synthesized audio are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{han2026safetywhombenchmarkingunderstanding, |
| title={Safety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants}, |
| author={Yewon Han and Jisu Jeon and Suhyun Choi and Juan Yeo and Minsoo Jo and Gihoon Kim and Hyunggyu Lim and Taesup Kim}, |
| year={2026}, |
| eprint={2610.17728}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2610.17728}, |
| } |
| ``` |
|
|
