README.md

---
language:
- en
license: cc-by-4.0
task_categories:
- video-text-to-text
tags:
- gaze
- multimodal
- video-understanding
- streaming-video
- temporal-reasoning
- proactive-understanding
- egocentric-vision
- visual-question-answering
---

# StreamGaze Dataset

[Paper](https://huggingface.co/papers/2512.01707) | [Project Page](https://streamgaze.github.io/) | [Code](https://github.com/daeunni/StreamGaze)

**StreamGaze** is a comprehensive streaming video benchmark for evaluating MLLMs on gaze-based QA tasks across past, present, and future contexts.

## 📁 Dataset Structure

```
streamgaze/
├── metadata/
│   ├── egtea.csv              # EGTEA fixation metadata
│   ├── egoexolearn.csv        # EgoExoLearn fixation metadata
│   └── holoassist.csv         # HoloAssist fixation metadata
│
├── qa/
│   ├── past_gaze_sequence_matching.json
│   ├── past_non_fixated_object_identification.json
│   ├── past_object_transition_prediction.json
│   ├── past_scene_recall.json
│   ├── present_future_action_prediction.json
│   ├── present_object_attribute_recognition.json
│   ├── present_object_identification_easy.json
│   ├── present_object_identification_hard.json
│   ├── proactive_gaze_triggered_alert.json
│   └── proactive_object_appearance_alert.json
│
└── videos/
    ├── videos_egtea_original.tar.gz         # EGTEA original videos
    ├── videos_egtea_viz.tar.gz              # EGTEA with gaze visualization
    ├── videos_egoexolearn_original.tar.gz   # EgoExoLearn original videos
    ├── videos_egoexolearn_viz.tar.gz        # EgoExoLearn with gaze visualization
    ├── videos_holoassist_original.tar.gz    # HoloAssist original videos
    └── videos_holoassist_viz.tar.gz         # HoloAssist with gaze visualization
```

## 🎯 Task Categories

### **Past (Historical Context)**
- **Gaze Sequence Matching**: Match gaze patterns to action sequences
- **Non-Fixated Object Identification**: Identify objects outside gaze
- **Object Transition Prediction**: Predict object state changes
- **Scene Recall**: Recall scene details from memory

### **Present (Current Context)**
- **Object Identification (Easy/Hard)**: Identify objects in/outside FOV
- **Object Attribute Recognition**: Recognize object attributes
- **Future Action Prediction**: Predict upcoming actions

### **Proactive (Future-Oriented)**
- **Gaze-Triggered Alert**: Alert based on gaze patterns
- **Object Appearance Alert**: Alert on object appearance

## 🚀 Quick Start

### Data Preparation

Download our dataset from HuggingFace and extract videos. The dataset should be located as below (note: the dataset itself is in `danaleee/StreamGaze`):

```
StreamGaze/
├── dataset/
│   ├── videos/
│   │   ├── original_video/        # Original egocentric videos
│   │   └── gaze_viz_video/        # Videos with gaze overlay
│   └── qa/
│       ├── past_*.json             # Past task QA pairs
│       ├── present_*.json          # Present task QA pairs
│       └── proactive_*.json        # Proactive task QA pairs
```

#### Extract Videos

```bash
# Extract EGTEA videos
tar -xzf videos_egtea_original.tar.gz -C videos/egtea/original/
tar -xzf videos_egtea_viz.tar.gz -C videos/egtea/viz/

# Extract EgoExoLearn videos
tar -xzf videos_egoexolearn_original.tar.gz -C videos/egoexolearn/original/
tar -xzf videos_egoexolearn_viz.tar.gz -C videos/egoexolearn/viz/

# Extract HoloAssist videos
tar -xzf videos_holoassist_original.tar.gz -C videos/holoassist/original/
tar -xzf videos_holoassist_viz.tar.gz -C videos/holoassist/viz/
```

### Running Evaluation

Quick evaluation on existing models:

```bash
# Evaluate ViSpeak (without gaze visualization)
bash scripts/vispeak.sh

# Evaluate ViSpeak (with gaze visualization)
bash scripts/vispeak.sh --use_gaze_instruction

# Evaluate GPT-4o
bash scripts/gpt4o.sh --use_gaze_instruction

# Evaluate Qwen2.5-VL
bash scripts/qwen25vl.sh --use_gaze_instruction
```

Results will be automatically computed and saved to:

```
results/
├── ModelName/
│   ├── results/              # Without gaze visualization
│   │   ├── *_output.json
│   │   └── evaluation_summary.json
│   └── results_viz/          # With gaze visualization
│       ├── *_output.json
│       └── evaluation_summary.json
```

## 🔑 Metadata Format

Each metadata CSV contains:
- `video_source`: Video identifier
- `fixation_id`: Fixation segment ID
- `start_time_seconds` / `end_time_seconds`: Temporal boundaries
- `center_x` / `center_y`: Gaze center coordinates (normalized)
- `representative_object`: Primary object at gaze point
- `other_objects_in_cropped_area`: Objects within FOV
- `other_objects_outside_fov`: Objects outside FOV
- `scene_caption`: Scene description
- `action_caption`: Action description 

## 📝 QA Format

Each QA JSON file contains:
```json
  {
    "response_time": "[00:08 - 09:19]",
    "questions": [
      {
        "question": "Among {milk, spoon, pan, phone}, which did the user never gaze at?",
        "time_stamp": "03:14",
        "answer": "A",
        "options": [
          "A. milk",
          "B. spoon",
          "C. pan",
          "D. phone"
        ],
      }
    ],
    "video_path": "OP01-R03-BaconAndEggs.mp4"
  }
```

## 📄 License

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

See https://creativecommons.org/licenses/by/4.0/

## 🔗 Links

- **Paper**: [https://huggingface.co/papers/2512.01707](https://huggingface.co/papers/2512.01707)
- **Evaluation code**: [https://github.com/daeunni/StreamGaze](https://github.com/daeunni/StreamGaze)
- **Project page**: [https://streamgaze.github.io/](https://streamgaze.github.io/)

## 📧 Contact

For questions or issues, please contact: daeun@cs.unc.edu