---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
tags:
- video
- multimodal
- segmentation
- pointing
- spatio-temporal-grounding
- robotics
- autonomous-driving
- cell-tracking
- egocentric-vision
- gui-interaction
---

# VPoS-Bench: Video Pointing and Segmentation Benchmark

**VPoS-Bench** is a challenging out-of-distribution benchmark designed to evaluate the spatio-temporal pointing and reasoning capabilities of video-language models. It covers a diverse set of five real-world application domains, with fine-grained point-level and segmentation annotations that enable robust evaluation of multimodal models under realistic, temporally complex scenarios.

> **Webpage**: [VideoMolmo](https://mbzuai-oryx.github.io/VideoMolmo/)

> **Paper**: [VideoMolmo: Spatio-Temporal Grounding meets Pointing](https://arxiv.org/pdf/2506.05336)

> **Model**: [VideoMolmo on Hugging Face](https://huggingface.co/ghazishazan/VideoMolmo)

> **Code**: [VideoMolmo on Github](https://github.com/mbzuai-oryx/VideoMolmo)

---

## 🌍 Benchmark Overview

VPoS-Bench tests the **generalization** of models in five diverse real-world scenarios:

1.  **Cell Tracking**  
    Track the trajectory of biological entities (e.g., nuclei or cells) across microscopy video frames.  
    > Applications: developmental biology, disease modeling

2.  **Egocentric Vision**  
    Identify and follow objects or hands in first-person camera footage.  
    > Applications: activity recognition, assistive tech

3.  **Autonomous Driving**  
    Point to traffic participants (pedestrians, vehicles, lights) under varying conditions.  
    > Applications: self-driving systems, urban scene understanding

4.  **Video-GUI Interaction**  
    Follow on-screen elements (e.g., cursors, buttons) across software interface recordings.  
    > Applications: AI-assisted UI navigation, screen agents

5.  **Robotics**  
    Track manipulable objects or robotic end-effectors as they interact in structured environments.  
    > Applications: robot learning, manipulation planning

---

## 📁 Dataset Structure
The dataset is organized by domain. Each domain folder contains three subdirectories:

-   `frames/` – Extracted video frames.
-   `masks/` – Segmentation masks corresponding to frames.
-   `annotations/` – JSON files containing text descriptions and point-level annotations.

```text
vpos-bench/
├── cell-tracking/
│   ├── frames/             # Extracted video frames (e.g., frame_0001.jpg, ...)
│   ├── masks/              # Segmentation masks per frame (optional)
│   └── annotations/        # Point coordinates + caption in JSON format
│
├── autonomous-driving/
...
---
├──
```

## 📁 Annotation Format
Each annotation is keyed by a unique video ID and consists of:

```json
{
  "video_id": {
    "caption": "natural language instruction here",
    "frames": [
      {
        "frame_path": "domain/frames/video_id/frame_00001.jpg",
        "mask_path": "domain/masks/video_id/0.png",
        "points": [[x, y], ...]
      },
      {
        "frame_path": "domain/frames/video_id/frame_00002.jpg",
        "mask_path": "domain/masks/video_id/1.png",
        "points": [[x, y], ...]
      }
    ]
  }
}
```