README.md

---
dataset_info:
  features:
  - name: task_id
    dtype: int32
  - name: annotation_id
    dtype: int32
  - name: image
    dtype: image
  - name: instruction_cn
    dtype: string
  - name: instruction_en
    dtype: string
  - name: sample_id
    dtype: string
  - name: gaze_point
    list: int32
  - name: choices
    struct:
    - name: activity
      dtype: string
    - name: is_same_window
      dtype: string
    - name: place
      dtype: string
    - name: platform
      dtype: string
    - name: scenario
      dtype: string
    - name: task type
      dtype: string
    - name: ui_type
      dtype: string
  - name: target_bbox
    struct:
    - name: height
      dtype: float32
    - name: image_rotation
      dtype: int32
    - name: labels
      list: string
    - name: original_height
      dtype: int32
    - name: original_width
      dtype: int32
    - name: rotation
      dtype: float32
    - name: width
      dtype: float32
    - name: x
      dtype: float32
    - name: y
      dtype: float32
  - name: is_ok
    dtype: bool
  - name: objects
    struct:
    - name: bbox
      list:
        list: float32
        length: 4
    - name: category
      list:
        class_label:
          names:
            '0': target
language:
- en
- zh
license: cc-by-4.0
tags:
- computer-vision
- visual-grounding
- xr
- egocentric
- gui
- apple-vision-pro
- instruction-following
- multimodal
task_ids:
- visual-grounding
- object-detection

pretty_name: EgoXR-GUI - Egocentric XR GUI Grounding Dataset
---
# EgoXR-GUI: Benchmarking GUI Grounding in Physical–Digital Extended Reality

EgoXR-GUI is the first extended reality (XR) specific GUI grounding benchmark. Unlike traditional desktop or mobile GUI benchmarks, EgoXR-GUI evaluates whether multimodal large language models (MLLMs) can effectively reason about virtual interfaces embedded within hybrid digital–physical environments.

## Overview

- **Dataset Size:** 1,070 carefully curated examples. (Originally comprising more internal annotations, the final publicly released benchmark validates exactly 1,070 high-quality target grounding instructions across diverse spatial scenarios.)
- **Platform:** Apple Vision Pro and other 3D/XR environments.
- **Task Types:** 
  1. **Direct Grounding:** Simple identification.
  2. **Spatial Grounding:** Reasoning about UI elements based on 3D spatial properties.
  3. **Semantic Grounding:** Reasoning based on the text or icon semantics of the UI elements.
- **Language Supported:** English (`instruction_en`) and Chinese (`instruction_cn`).

## Data Fields

Each Example contains the following fields:
- `task_id` & `annotation_id`: Unique identifiers for tracking the specific visual task.
- `sample_id`: External sample identifier linking back to the origin dataset source.
- `image`: The egocentric view captured from the XR headset/environment.
- `instruction_en`: The grounding prompt in English.
- `instruction_cn`: The grounding prompt in Chinese.
- `gaze_point`: The tracked eye gaze coordinate `[x, y]` representing the user's attention.
- `choices`: Structured dictionary showing context tags:
  - `is_same_window`
  - `ui_type`
  - `platform` 
  - `scenario`
  - `place`
  - `activity`
  - `task type`
- `target_bbox`: The exact geometrical target. Contains `x`, `y`, `width`, `height`, spatial `rotation`, and string `labels`.
- `objects`: Hugging Face standardized format representing the bounding box for Data Viewer Visualization.
- `is_ok`: Quality control boolean indicator.