--- 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//` 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/.mp4` and `images///`. | | `goal` | `str` | Natural-language task goal (the tutorial video title). | | `img_filename` | `str` | Relative frame path without extension, `"/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.