---
license: cc-by-4.0
language:
- en
tags:
- video
- multimodal
- audio
- audio-visual-localization
size_categories:
- 1B<n<10B
pretty_name: AVATAR
---

# AVATAR: What’s Making That Sound Right Now? Video-centric Audio-Visual Localization

**AVATAR** stands for **A**udio-**V**isual localiz**A**tion benchmark for a spatio-**T**empor**A**l pe**R**spective in video.

AVATAR is a **benchmark dataset** designed to evaluate **video-centric audio-visual localization (AVL)** in **complex and dynamic real-world scenarios**.  
Unlike previous benchmarks that rely on static image-level annotations and assume simplified conditions, AVATAR offers **high-resolution temporal annotations** over entire videos. It supports four challenging evaluation settings:  
**Single-sound**, **Mixed-sound**, **Multi-entity**, and **Off-screen**.

📄 [Paper (ICCV 2025)](https://arxiv.org/abs/2507.04667)  
🌐 [Project Website](https://hahyeon610.github.io/Video-centric_Audio_Visual_Localization/)  
📁 [Code & Data Viewer](https://huggingface.co/datasets/mipal/AVATAR/tree/main)

---

## 📦 Dataset Structure

The dataset consists of the following files:

| File | Description |
|------|-------------|
| `video.zip` | ~3.8GB of `.mp4` video clips |
| `metadata.zip` | ~1.6GB of annotations (bounding boxes, segmentation masks, scenario tags) |
| `vggsound_10k.txt` | List of 10,000 training video IDs from [VGGSound](https://huggingface.co/datasets/Loie/VGGSound)|
| `code/` | AVATAR benchmark evaluation code |

Each annotated frame includes:
- Visual bounding boxes and segmentation masks for sound-emitting objects
- Audio-visual category labels aligned to the active sound source at each timestamp
- Instance-level scenario labels (e.g., Off-screen, Mixed-sound)

---

## 📊 Dataset Statistics

AVATAR provides detailed quantitative statistics to help users understand its scale and diversity.

| Type       | Count  |
|------------|--------|
| Videos     | 5,000  |
| Frames     | 24,266 |
| Off-screen | 670    |

| Scenario Type   | Instances |
|-----------------|-----------|
| Total           | 28,516    |
| Single-sound    | 15,372    |
| Multi-entity    | 9,322     |
| Mixed-sound     | 3,822     |

---

## 🧪 Scenarios and Tasks

AVATAR supports **fine-grained scenario-wise evaluation** of AVL models:

1. **Single-sound**: One sound-emitting instance per frame
2. **Mixed-sound**: Multiple overlapping sound sources (same or different categories)
3. **Multi-entity**: One sounding instance among multiple visually similar ones
4. **Off-screen**: No visible sound source within the frame

🔍 You can evaluate your model using:
- **Consensus IoU (CIoU)**
- **AUC**
- **Pixel-level TN% (for Off-screen)**

---

## 🧩 Audio-Visual Category Diversity

AVATAR spans **80 audio-visual categories** covering a wide range of everyday domains, including:
- **Human activities** (e.g., talking, singing)
- **Music performances** (e.g., violin, drum, piano)
- **Animal sounds** (e.g., dog barking, bird chirping)
- **Vehicles** (e.g., car engine, helicopter)
- **Tools and machines** (e.g., chainsaw, blender)

Such diversity enables a **comprehensive evaluation** of model generalizability across varied audio-visual contexts.

---

## 📝 Example Metadata Format
```json
{
  "video_id": str,
  "frame_number": int,
  "annotations": [
    { // instance 1 (e.g., man)
      "segmentation": [ // (x, y) annotated RLE format
            [float, float], 
            ...
          ],
      "bbox": [float, float, float, float], // (l, t, w, h),
      "scenario": str, // "Single-sound", "Mixed-sound", "Multi-entity", "Off-screen"
      "audio_visual_category": str,
    },
    { // instance 2 (e.g., piano)
      ...
    }, 
    ...
  ]
}
```

---

## 📚 Citation
```bibtex
@InProceedings{Choi_2025_ICCV,
    author    = {Choi, Hahyeon and Lee, Junhoo and Kwak, Nojun},
    title     = {What's Making That Sound Right Now? Video-centric Audio-Visual Localization},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {20095-20104}
}
```