Datasets:
File size: 3,519 Bytes
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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.
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