README.md

metadata

pretty_name: EgoIntent
language:
  - en
task_categories:
  - video-text-to-text
size_categories:
  - 1K<n<10K
source_datasets:
  - extended
tags:
  - video
  - egocentric-video
  - intent-understanding
  - procedural-reasoning
  - action-anticipation
  - multimodal
configs:
  - config_name: default
    drop_labels: true

EgoIntent

Dataset Summary

EgoIntent is an open-ended benchmark for understanding intent at pre-outcome micro-steps in egocentric procedural activities. Given only the visual evidence available before a step's key outcome, a model must infer:

1. Local Intent (What): the actor's immediate goal in the current micro-step.
2. Procedural Intent (Why): how the current micro-step advances the broader procedure.
3. Next-Plan (Next): the action most likely to occur immediately afterward.

The benchmark contains 3,014 manually annotated micro-step clips constructed from 32 Ego4D source videos/event directories across 15 indoor and outdoor daily-life scenarios. The activities include cooking, cleaning, organizing, repairing, painting, gardening, and outdoor manual work.

Each released MP4 ends at a manually selected pre-outcome cutoff. Outcome-revealing frames and subsequent actions are excluded from the model input, while the immediate observed continuation is retained only as a text target.

Benchmark at a Glance

Item	Count
Pre-outcome MP4 clips / micro-steps	3,014
JSON annotation files	32
Ego4D source videos / event directories	32
Scenarios	15
Indoor scenarios	10
Outdoor scenarios	5
Total repository data	approximately 17 GB

There are no predefined train, validation, or test splits in this release.

Task Definition

For each micro-step, the input is the egocentric observation from start_time to obs_end_time. A model generates three open-ended text predictions:

• Local Intent: purpose at the current micro-step level, rather than a literal paraphrase of visible motion.
• Procedural Intent: the functional role of the step in the surrounding activity.
• Next-Plan: the immediate action expected after the observed clip.

The three targets are related but non-redundant. Because valid descriptions may differ in wording, and multiple futures can be reasonable, evaluation should use semantic agreement rather than exact string matching.

Directory Structure

EgoIntent/
├── metadata.jsonl
├── indoor/
│   └── art_studio/
│       └── draw/
│           ├── step1.mp4
│           ├── step2.mp4
│           ├── ...
│           └── step_label.json
└── outdoor/
    └── garden/
        └── cut_trees/
            ├── step1.mp4
            ├── step2.mp4
            ├── ...
            └── step_label.json

Paths follow:

{setting}/{scene}/{event}/step{step_id}.mp4
{setting}/{scene}/{event}/step_label.json

where setting is indoor or outdoor.

Dataset Viewer Metadata

The root-level metadata.jsonl contains one row per MP4 and links the video through its relative file_name. Hugging Face renders that field as the video column. The remaining Viewer columns are:

• id: a globally unique ID in the form {setting}/{scene}/{event}/step{step_id};
• local_intent;
• procedural_intent;
• observed_next_step;
• plausible_next_steps.

The Dataset Card configuration explicitly sets drop_labels: true, so event directory names are not interpreted as classification labels.

Annotation Format

Each event directory contains one step_label.json file:

{
  "video_uid": "8bef8b33-a1ee-43e7-a2d0-8cf68653cb0c",
  "scene": "art_studio",
  "event": "draw",
  "steps": [
    {
      "step_id": 1,
      "start_time": 0,
      "obs_end_time": 0.12332,
      "local_intent": "hold the sketch paper",
      "procedural_intent": "align reference for painting",
      "observed_next_step": "release the paper on the table",
      "plausible_next_steps": [
        "lift the sketch paper",
        "view the phone screen"
      ]
    }
  ]
}

Field Definitions

Field	Type	Description
video_uid	string	Identifier of the Ego4D source video.
scene	string	Daily-life scenario associated with the source video.
event	string	Procedural activity/event directory name.
steps	list	Micro-step annotations for this source video/event.
step_id	integer	Step identifier used by the matching step{step_id}.mp4 file.
start_time	number	Start timestamp of the observation in the source video, in seconds.
obs_end_time	number	Manually selected pre-outcome cutoff timestamp, in seconds.
local_intent	string	Immediate goal pursued in the current micro-step.
procedural_intent	string	Role of the micro-step in the broader procedure.
observed_next_step	string	Immediate continuation observed after the cutoff; used as the canonical Next-Plan target.
plausible_next_steps	list of strings	Other reasonable continuations supported by the pre-outcome evidence.

Scenario Composition

Setting	Scenario	Event directories	Micro-steps
Indoor	art_studio	draw	90
Indoor	bedroom	clean_windows, sort_clothes	191
Indoor	clothing_closet	iron_clothes	104
Indoor	garage	assemble_wheels, repair_bicycle, repair_car	321
Indoor	hallway	paint_wall	101
Indoor	kitchen	bake_pastry, boil_noodles, knead_dough, make_cakes, wash_dishes	396
Indoor	laundry_room	wash_clothes, wash_clothes_2	106
Indoor	living_room	eat, organize_dishes	198
Indoor	study_room	sort_books, tidy_up	202
Indoor	workshop	assemble_mower, assemble_wood, examine_engine, layout, make_crafts	459
Outdoor	deck	measure	106
Outdoor	farm	farm_work, pick_fruits	214
Outdoor	garden	cut_branches, cut_trees, paint_timber	309
Outdoor	space	draw_flowers	113
Outdoor	yard	cut_sticks	104
Total	15 scenarios	32 event directories	3,014

Loading the Data

Download the complete repository:

from huggingface_hub import snapshot_download

dataset_root = snapshot_download(
    repo_id="py2279105943/EgoIntent",
    repo_type="dataset",
)

To download only one event:

from huggingface_hub import snapshot_download

event_root = snapshot_download(
    repo_id="py2279105943/EgoIntent",
    repo_type="dataset",
    allow_patterns=["indoor/art_studio/draw/*"],
)

Read annotations and resolve the matching clip:

import json
from pathlib import Path

event_dir = Path(dataset_root) / "indoor" / "art_studio" / "draw"
annotations = json.loads((event_dir / "step_label.json").read_text())

for step in annotations["steps"]:
    video_path = event_dir / f"step{step['step_id']}.mp4"
    print(video_path, step["local_intent"])

This repository uses a hierarchical benchmark layout rather than a packaged datasets builder. Loading with snapshot_download preserves the paths needed to pair every MP4 with its event-level JSON annotations.

Construction and Quality Control

Source videos were selected for coherent procedural transitions and clear hand-object interactions. Annotators manually segmented each source video into micro-steps, identified the outcome completing each immediate goal, and placed the observation boundary immediately before that outcome became visually explicit.

Intent labels and temporal boundaries underwent multiple rounds of human review. Reviewers checked that each micro-step expressed one coherent immediate goal, stayed within the intended temporal limit, ended before the key outcome, and maintained distinct abstraction levels across Local Intent, Procedural Intent, and Next-Plan.

Intended Uses

EgoIntent is intended for research on:

• egocentric video-language understanding;
• step-level intent and goal inference;
• procedural reasoning;
• action anticipation;
• temporal-context and boundary-cue analysis;
• evaluation of multimodal large language models.

The dataset is not intended for identity recognition, surveillance, or inferring sensitive personal attributes.

Limitations

• The benchmark contains 32 source videos, so multiple micro-steps share the same source context.
• No official train/validation/test split is provided. If constructing splits, separate examples by video_uid—and preferably by scene or event—to reduce source-video leakage.
• Labels are written in English and may reflect annotator wording preferences.
• Next actions can be inherently ambiguous. plausible_next_steps captures some alternatives but is not exhaustive.
• The benchmark focuses on procedural daily-life activities and should not be treated as representative of all human intentions, environments, or cultures.
• The videos originate from Ego4D; users should consider the privacy, geographic, demographic, and collection biases documented for the source dataset.

License and Usage Terms

No standalone license is declared for this repository at the time of this release. The underlying videos originate from Ego4D and remain subject to the applicable Ego4D license and usage terms. Users should verify those terms and confirm annotation reuse conditions with the dataset maintainers before redistribution or commercial use.

Citation

Citation information will be added after the associated paper is publicly available.

Contact

Questions, corrections, and annotation issues can be reported through the Hugging Face repository's Community tab.