| --- |
| 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. |
|
|
|
|
