facebook/wearable-ai

Wearable AI Dataset (ECCV 2026)

Part of the Wearable AI Workshop at ECCV 2026.

A benchmark of egocentric (first-person, head-mounted wearable camera) videos paired with three complementary video question-answering tasks for evaluating wearable-AI assistants on real-world everyday activity videos.

▶ Baseline code & evaluation scripts: see starter_kit/README.md. The starter kit ships inside this repo, so git clone gives you the code and the data together.

Tasks

EgoLongQA — Long-form video question answering with multiple-choice answers. Given a video and a question with four options (A/B/C/D), predict the correct answer.

EgoConv — Conversational video question answering. Given a video and a multi-turn conversation, generate free-form answers for each turn. The model sees only the video up to the current turn (no future leaking) and uses its own previous answers as context.

EgoProactive — Proactive assistant over streaming egocentric video. Given a video stream, the model must decide when to speak and what to say at each candidate moment, simulating a wearable AI that volunteers helpful information without being prompted.

Configurations

Config	Split	# samples	Description
egoconv	val	700	Multi-turn conversational QA grounded in egocentric video
egolongqa	val	700	Long-video MCQ over single, longer egocentric clips
egoproactive	val	700	Proactive-assistant moments over streaming egocentric video

Repo Layout

facebook/wearable-ai/
├── README.md                   # this file
├── LICENSE                     # CC-BY-NC-4.0 (dataset)
├── egoconv/
│   ├── wearable_ai_2026_egoconv_val_700.jsonl
│   └── val/<id>.mp4            # 700 videos, ~91 GB
├── egolongqa/
│   ├── wearable_ai_2026_egolongqa_val_700.jsonl
│   └── val/<id>.mp4            # 700 videos, ~203 GB
├── egoproactive/
│   ├── wearable_ai_2026_egoproactive_val_700.jsonl
│   └── val/<id>.mp4            # 700 videos, ~23 GB
└── starter_kit/                # baseline code + evaluation scripts
    ├── README.md
    ├── LICENSE                 # CC-BY-NC-4.0 (code)
    ├── run_evaluation.py
    └── ...

Cloning & Disk Usage

The full dataset is ~317 GB across 2,100 videos. Pick the recipe that matches what you need:

Full clone (code + annotations + all videos, ~317 GB):

git clone https://huggingface.co/datasets/facebook/wearable-ai

Code + annotations only (~25 MB; videos left as LFS pointers, downloadable on demand):

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/facebook/wearable-ai

One task only (annotations + the task's videos):

huggingface-cli download facebook/wearable-ai \
    --repo-type dataset \
    --include "egolongqa/**" "starter_kit/**" "*.md" "LICENSE" \
    --local-dir wearable-ai

Per-task video sizes: egoconv ≈ 91 GB · egolongqa ≈ 203 GB · egoproactive ≈ 23 GB.

Loading

from datasets import load_dataset

egoconv_val = load_dataset("facebook/wearable-ai", "egoconv", split="val")
egolongqa_val = load_dataset("facebook/wearable-ai", "egolongqa", split="val")
egoproactive_val = load_dataset("facebook/wearable-ai", "egoproactive", split="val")

Schemas

egoconv — Conversational QA over egocentric video

Field	Type	Description
video_path	str	Basename of the video file (<id>.mp4)
duration_in_sec	float	Video duration in seconds
video_intervals	list[[float, float]]	Time intervals (start, end) covered by the conversation
questions	list[str]	Sequence of user questions over the video
answers	list[str]	Reference answers, aligned to questions
task	str	Coarse activity tag (e.g. Tourism, Cooking)
dialog	list[dict]	Raw turn-by-turn dialog. Each turn: `{text: str, role: "P0"

egolongqa — Long-form MCQ over egocentric video

Field	Type	Description
video_path	str	Basename of the video file (<id>.mp4)
question	str	Long-form question over the full video
answer	str	Open-ended reference answer
mcq_options	str	Formatted MCQ choices: A. ... B. ... C. ... D. ...
mcq_answer	str	Correct option letter (A/B/C/D)
category	str	Activity category (e.g. Travel-Sightseeing (Indoors))

egoproactive — Proactive assistant over streaming egocentric video

Field	Type	Description
video_path	str	Basename of the video file (<id>.mp4)
duration_in_sec	float	Video duration in seconds
video_intervals	list[[float, float]]	Candidate decision intervals (start, end) over the streaming video
query	str	The user's initial high-level query (e.g. "How do I decorate a notebook cover with stickers?")
domain	str	Coarse domain tag (e.g. Arts and Crafts, Cooking)
task	str	Specific task description (e.g. "Decorating a notebook cover with stickers")
answers	list[str]	Reference assistant decisions, one per interval. Each entry is either "$silent$" (stay silent) or "$interrupt$<utterance>" (speak with the given utterance)
dialog	list[list[dict]]	Cumulative dialog state before each interval. Each turn: `{role: "user"

Videos

Video files are bundled in this repository under each config's val/ folder. The video_path field in each row is the basename of a .mp4 file located at:

<repo_root>/<config>/val/<video_path>

where <config> is the config name (egoconv, egolongqa, or egoproactive). The repo holds 2,100 videos (700 per task, ~317 GB total). Videos are H.265 / 1080p / 15 fps, audio-stripped, and face-blurred. See Cloning & Disk Usage above for download recipes.

Example: looking up a video locally after downloading the repo snapshot:

import os
from huggingface_hub import snapshot_download

repo_root = snapshot_download("facebook/wearable-ai", repo_type="dataset")
sample = egoconv_val[0]
video_file = os.path.join(repo_root, "egoconv", "val", sample["video_path"])

License

The dataset in this repository (videos + JSONL annotations) is released under CC-BY-NC-4.0 — see LICENSE.

The starter-kit code under starter_kit/ is separately licensed under CC-BY-NC-4.0 — see starter_kit/LICENSE.

Both licenses permit academic / research use only; commercial use is not granted. Pre-trained model weights downloaded from HuggingFace (e.g., Llama 4 Scout, Llama 4 Maverick, Qwen2.5-VL) are governed by their respective licenses on the model pages.

Citation

@misc{wearableaiworkshop2026,
  title = {Wearable AI Workshop at ECCV 2026},
  author = {Tuyen (Harry) Tran and Maxim Arap and Seungwhan Moon and Raffay Hamid and Alessandro Suglia and Zsolt Kira and Pascale Fung and Mubarak Shah},
  year = {2026},
  howpublished = {\url{https://wearable-ai-workshop.github.io/}},
  note = {Workshop at the European Conference on Computer Vision (ECCV) 2026}
}