README.md

---
license: mit
task_categories:
- image-text-to-text
language:
- en
tags:
- gui
- agent
- gui-agent
- video
- video-guided
- benchmark
- mobile
- eccv2026
pretty_name: VG-GUI-Bench
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: ours_data.json
---

# VG-GUI-Bench

**Video-Guided GUI Agent Benchmark** — the benchmark contribution of our ECCV 2026 paper
**[Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction](https://arxiv.org/abs/2606.29445)**.

[![arXiv](https://img.shields.io/badge/arXiv-2606.29445-B31B1B.svg?logo=arxiv)](https://arxiv.org/abs/2606.29445)
[![Project Page](https://img.shields.io/badge/Project-Page-blue.svg?logo=github)](https://vg-gui-tasker.github.io/)
[![Code](https://img.shields.io/badge/Code-GitHub-181717.svg?logo=github)](https://github.com/VG-GUI-TASKER/VG-GUI-TASKER)

> **Authors.** Sunqi Fan, Qingle Liu, Runqi Yin, Meng-Hao Guo, Shuojin Yang (Tsinghua University).

## What is VG-GUI-Bench?

Recent Multimodal Large Language Models (MLLMs) achieve strong results on Video Question Answering
(VideoQA), but existing benchmarks mostly probe *shallow visual perception* and rarely test whether a
model can **learn a procedure from a tutorial video and carry it out** as a long-horizon interactive
task. VG-GUI-Bench closes this gap.

Given a **YouTube tutorial video** that demonstrates how to accomplish a task on a mobile device
(e.g. *"How To Change Discord Password"*), a GUI agent must:

1. **Understand the workflow** from reference frames drawn from the tutorial video (scene keyframes,
   uniform samples, annotated frames, or algorithmically searched keyframes);
2. **Localize the current progress** from its previous action history and the current screen; and
3. **Predict the exact next action** — one of `CLICK`, `SCROLL`, `TYPE`, `PRESS`, `ZOOM`, `FINISH` —
   on the current screen.

This makes VG-GUI-Bench a testbed for **video in-context learning**: transferring procedural knowledge
from an instructional video to grounded, step-by-step decision making.

This dataset is a **processed and re-annotated version built on top of the
[MONDAY](https://huggingface.co/datasets/runamu/MONDAY) dataset**, curated and packaged for the
video-guided GUI agent evaluation described in the paper.

## Dataset Statistics

| Item | Value |
|------|-------|
| Episodes (tutorial videos / tasks) | 100 |
| Total annotated steps | 1,071 |
| Average steps per episode | 10.7 |
| Source videos | 98 mobile-app tutorial videos (YouTube) |
| Reference-frame variants per episode | 8 (see below) |
| Total size | ~3 GB |

### Action type distribution

| `action_type_id` | `action_type_text` | Count |
|:---:|---|---:|
| 4 | `click` | 1,934 |
| 4 | `scroll down` | 175 |
| 4 | `scroll up` | 27 |
| 4 | `scroll right` | 13 |
| 4 | `scroll left` | 12 |
| 3 | `type` | 50 |
| 6 | `press home` | 39 |
| 5 | `press back` | 36 |
| 12 | `zoom or multi-touch` | 12 |
| 14 | `other hardware` | 11 |

## Repository Layout

```
VG-GUI-Bench/
├── ours_data.json                 # Step-level action annotations (main label file)
├── ytb_video/                     # Source tutorial videos, one .mp4 per episode (keyed by ep_id)
│   ├── 07hF8RAFgIc.mp4
│   └── ...
└── images/                        # Pre-rendered frames, per reference mode → per episode → frames
    ├── origin/                    #   Scene-timestamp keyframes (cropped to phone screen)
    ├── origin_no_cut/             #   Scene-timestamp keyframes (full frame)
    ├── annotation/                #   Frames with a red bounding box on the target (cropped)
    ├── annotation_no_cut/         #   Frames with a red bounding box on the target (full frame)
    ├── uniform_5/                 #   5 uniformly sampled frames (cropped)
    ├── uniform_5_no_cut/          #   5 uniformly sampled frames (full frame)
    ├── uniform_10/                #   10 uniformly sampled frames (cropped)
    └── uniform_10_no_cut/         #   10 uniformly sampled frames (full frame)
```

Inside every `images/<mode>/` directory the frames are grouped by episode id, e.g.
`images/origin/07hF8RAFgIc/frame_0000.png`, `frame_0001.png`, …

## Data Schema

`ours_data.json` is a single JSON object with one key, `"ours"`, whose value is a **list of 100
episodes**. Each **episode** is a **list of steps**, and each **step** has the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `ep_id` | `str` | YouTube video id; also the key linking to `ytb_video/<ep_id>.mp4` and `images/<mode>/<ep_id>/`. |
| `goal` | `str` | Natural-language task goal (the tutorial video title). |
| `img_filename` | `str` | Relative frame path without extension, `"<ep_id>/frame_XXXX"`; the current screen for this step. |
| `action_list` | `list[Action]` | One or more ground-truth action candidates for this step (see below). |

Each **`Action`** object:

| Field | Type | Description |
|-------|------|-------------|
| `action_type_id` | `int` | Numeric action type (see the distribution table above). |
| `action_type_text` | `str` | Human-readable action type, e.g. `click`, `scroll down`, `type`, `press back`, `press home`, `zoom or multi-touch`. |
| `annot_position` | `list[float]` | Normalized bounding box(es) of the target UI element, as a flat list of `[x, y, w, h]` quadruples (0.0–1.0). Multiple quadruples may be concatenated when several equivalent targets are annotated. |
| `touch` | `[float, float]` | Normalized `(x, y)` gesture start point (0.0–1.0). |
| `lift` | `[float, float]` | Normalized `(x, y)` gesture end point (0.0–1.0). For `click` this equals `touch`; for scrolls it differs. |
| `type_text` | `str` | The text to enter for `type` actions; empty string otherwise. |

All coordinates are **normalized to `[0, 1]`** relative to the screen width/height.

### Minimal example

```json
{
  "ep_id": "SIjOxM9jVj8",
  "goal": "How To Change Discord Password 2021 | Discord Mobile App",
  "img_filename": "SIjOxM9jVj8/frame_0000",
  "action_list": [
    {
      "action_type_id": 4,
      "action_type_text": "click",
      "annot_position": [0.862, 0.079, 0.062, 0.116],
      "touch": [0.137, 0.893],
      "lift":  [0.137, 0.893],
      "type_text": ""
    }
  ]
}
```

## Reference-Frame Modes

The benchmark studies how different visual-context strategies affect agent performance. The pre-rendered
`images/` directories cover **8** of these modes (`origin`, `annotation`, `uniform_5`, `uniform_10`, each
in `cut` / `no_cut` variants). The evaluation code additionally supports algorithmic keyframe-search modes
(`tasker`, `bfs`, `gbfs`, `dijkstra`) and video-agent modes (`videoagent`, `videotree`), which are produced
on demand from the source videos — see the [code repository](https://github.com/VG-GUI-TASKER/VG-GUI-TASKER).

## Usage

```python
import json
from huggingface_hub import snapshot_download

local_dir = snapshot_download(repo_id="Aoraku/VG-GUI-Bench", repo_type="dataset")

data = json.load(open(f"{local_dir}/ours_data.json"))["ours"]
print(len(data), "episodes")
for step in data[0]:                     # iterate over one episode
    print(step["img_filename"], step["action_list"][0]["action_type_text"])
```

For the full data-processing pipeline, reference-mode generation, and the four evaluation metrics
(Accuracy, Completion, Efficiency, PIR), see the official code:
**https://github.com/VG-GUI-TASKER/VG-GUI-TASKER** (`VG-GUI-Bench/`).

## License

Released under the [MIT License](https://lbesson.mit-license.org/). The underlying videos remain the
property of their respective YouTube uploaders and are provided for research use only.

## Citation

```bibtex
@inproceedings{fan2026bridging,
  title     = {Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction},
  author    = {Fan, Sunqi and Liu, Qingle and Yin, Runqi and Guo, Meng-Hao and Yang, Shuojin},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```

## Acknowledgements

Built on top of the [MONDAY](https://huggingface.co/datasets/runamu/MONDAY) dataset. We thank its authors
for releasing the original data.