WildGUI

This repository hosts a personally reprocessed annotation release for WildGUI, the dataset introduced by Video2GUI.

The original Video2GUI project builds WildGUI from large-scale Internet tutorial videos for GUI agent pretraining. This repository focuses on the open annotation artifacts: the records were regenerated and cleaned following the full annotation workflow, then reformatted to make the data easier to inspect, reuse, and reproduce. It also ships the screenshot frames that the trajectories reference.

What's in this repository

Content	Coverage	Location
Annotations (JSONL)	part1–part19 (all parts)	wildgui_part{N}.jsonl
Screenshots (tar shards)	part1–part15	screenshots/part{N}/

The screenshots for the remaining parts (part16–part19) are hosted in a companion repository: joker-112/WildGUI_Screenshots. So the annotations for every part live here, while their screenshot frames are split between this repo (part1–part15) and the companion repo (part16–part19).

Annotation File Layout

The processed annotation release is organized as JSONL shards, one per part:

wildgui_part1.jsonl
wildgui_part2.jsonl
...
wildgui_part19.jsonl

Each line is one task-level JSON object. The original multi-task annotations entries were split by task_id, so each record contains a single GUI task and its ordered action trajectory.

Record Structure

Top-level fields:

Field	Type	Description
video_id	string	Source video identifier.
segment_index	integer	Segment index within the source video.
task_id	integer	Task identifier after splitting the original annotations.
instruction	string	Natural-language task instruction.
dense_caption	string	Dense natural-language summary of the GUI behavior.
plan	string	High-level task plan.
platform	string	Platform context, such as web, desktop, or mobile.
software	string	Application or software used in the interaction.
website	string	Website or web service, when applicable.
trajectories	list	Ordered GUI action sequence for the task.

Each trajectories item is an action object. Common action fields include:

Field	Type	Description
timestamp	string	Action timestamp in the source video segment.
action_type	string	Action category, such as moveTo, click, typing, scrolling, or keyboard operation.
grounding_instruction	string	Natural-language description of the target GUI element or action target.
action_reason	string	Reason for performing the action.
action_parameters	object, optional	Grounded parameters such as point, bbox, text, key, start_point, end_point, direction, or duration fields. This field is omitted when reliable grounding is unavailable.
core_change_reason	string	Explanation of the expected or observed GUI state change.
core_change	string	Description of the main GUI state change after the action.
effects_on_success	string, optional	Additional note on how the action affects task completion.
finish_reason	string, optional	Reason the trajectory or task is considered finished.
use_grounding	boolean	Whether the grounded action parameters should be used.

Example

{
  "video_id": "...",
  "segment_index": 0,
  "task_id": 0,
  "instruction": "Use a component library to find a design element and apply it to an AI app builder.",
  "dense_caption": "...",
  "plan": "step1: ...",
  "platform": "web",
  "software": "Google Chrome",
  "website": "example.com",
  "trajectories": [
    {
      "timestamp": "00:18",
      "action_type": "moveTo",
      "grounding_instruction": "Move the mouse cursor over the target component.",
      "action_reason": "...",
      "core_change_reason": "...",
      "core_change": "...",
      "use_grounding": false
    }
  ]
}

Screenshots (part1–15)

Screenshot frames are grouped by the same partN shards and packed into uncompressed tar archives (the frames are already JPEG-compressed):

screenshots/
  part1/
    wildgui_part1_images_000001.tar
    wildgui_part1_images_000002.tar
    ...
  part2/
    ...
  ...
  part15/
    ...

Inside each tar, every frame is stored under a per-video directory:

{video_id}/screenshot_{MM_SS}.jpg

{MM_SS} is the action timestamp normalized to zero-padded minutes_seconds (e.g. the annotation timestamp "00:18" → screenshot_00_18.jpg).

Linking an annotation to its screenshot

Each trajectory action maps to exactly one frame. Given a record's video_id and an action's timestamp:

1. Take the partN matching the annotation shard (e.g. wildgui_part3.jsonl → screenshots/part3/). For part16–part19, fetch the frames from joker-112/WildGUI_Screenshots instead.
2. Normalize the timestamp to MM_SS: timestamps like "00:18", "1:05", or a raw second count are converted to total minutes_seconds, each part zero-padded to two digits.
3. The frame is {video_id}/screenshot_{MM_SS}.jpg, found inside one of that part's wildgui_part{N}_images_*.tar shards.

def timestamp_to_suffix(ts: str) -> str:
    """'00:18' -> '00_18', '1:05' -> '01_05', '78' -> '01_18'."""
    ts = str(ts).strip()
    if ":" in ts:
        total = 0
        for part in ts.split(":"):
            total = total * 60 + int(part)
    else:
        total = int(float(ts))
    minutes, seconds = divmod(total, 60)
    return f"{minutes:02d}_{seconds:02d}"

A small fraction of annotated frames may be missing from the packed shards (source frame unavailable at pack time); treat a missing {video_id}/screenshot_{MM_SS}.jpg as a skippable example rather than an error.

Loading

Annotations:

from datasets import load_dataset

dataset = load_dataset(
    "xwm/WildGUI",
    data_files="wildgui_part*.jsonl",
    split="train",
)

For local files:

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="/path/to/wildgui_part*.jsonl",
    split="train",
)

Screenshots (download and unpack one part):

hf download xwm/WildGUI \
  --repo-type dataset \
  --include "screenshots/part1/*" \
  --local-dir ./wildgui

for t in ./wildgui/screenshots/part1/*.tar; do
  tar -xf "$t" -C ./wildgui_frames
done

Intended Use

WildGUI is intended for research on GUI agents, GUI grounding, action prediction, interaction trajectory modeling, and multimodal agent pretraining.

The annotations are automatically derived from tutorial videos and may contain noise. Users should validate the data for their own downstream training or evaluation settings, especially when relying on spatial grounding. For actions with use_grounding: false, use the natural-language action context but avoid treating removed coordinates or parameters as valid supervision.

Citation

If you use this reprocessed annotation release, please cite the Video2GUI paper:

@misc{xiong2026video2gui,
  title = {Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining},
  author = {Xiong, Weimin and Gu, Shuhao and Ye, Bowen and Yue, Zihao and Li, Lei and Song, Feifan and Li, Sujian and Tian, Hao},
  year = {2026},
  eprint = {2605.14747},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  doi = {10.48550/arXiv.2605.14747},
  url = {https://arxiv.org/abs/2605.14747}
}