README.md

---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
tags:
- video
- text
- Robotics
- Autonomous Driving
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: Video
    dtype: string
  - name: Source
    dtype: string
  - name: Task
    dtype: string
  - name: QType
    dtype: string
  - name: Question
    dtype: string
  - name: Prompt
    dtype: string
  - name: time_start
    dtype: float64
  - name: time_end
    dtype: float64
  - name: Candidates
    struct:
    - name: A
      dtype: string
    - name: B
      dtype: string
    - name: C
      dtype: string
    - name: D
      dtype: string
    - name: E
      dtype: string
  - name: Answer
    dtype: string
  - name: Answer Detail
    dtype: string
  - name: ID
    dtype: int64
  - name: scene
    dtype: string
  splits:
  - name: test
    num_bytes: 1299057
    num_examples: 2064
  download_size: 392237
  dataset_size: 1299057
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---


# [ICCV 2025] Spatial-Temporal Intelligence Benchmark (STI-Bench)

<div style="text-align: center">
  <a href="https://arxiv.org/abs/2503.23765"><img src="https://img.shields.io/badge/arXiv-2503.23765-b31b1b.svg" alt="arXiv"></a>
  <a href="https://huggingface.co/datasets/MINT-SJTU/STI-Bench"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue" alt="Hugging Face Datasets"></a>
  <a href="https://github.com/MINT-SJTU/STI-Bench"><img src="https://img.shields.io/badge/GitHub-Code-lightgrey" alt="GitHub Repo"></a>
  <a href="https://mint-sjtu.github.io/STI-Bench.io/"><img src="https://img.shields.io/badge/Homepage-STI--Bench-brightgreen" alt="Homepage"></a>
</div>
<div style="text-align: center">
  <a href="https://mp.weixin.qq.com/s/yIRoyI1HbChLZv4GuvI7BQ"><img src="https://img.shields.io/badge/量子位-red" alt="量子位"></a>
  <a href="https://app.xinhuanet.com/news/article.html?articleId=8af447763b11efc491455eb93a27eac0"><img src="https://img.shields.io/badge/新华网-red" alt="新华网"></a>
    <a href="https://mp.weixin.qq.com/s/pVytCfXmcG-Wkg-sOHk_BA"><img src="https://img.shields.io/badge/PaperWeekly-red" alt="PaperWeekly"></a>
</div>


This repository contains the Spatial-Temporal Intelligence Benchmark (STI-Bench), introduced in the paper [“STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?”](https://arxiv.org/abs/2503.23765), which evaluates the ability of Multimodal Large Language Models (MLLMs) to understand spatial-temporal concepts through real-world video data.

## Files



```bash
# Make sure git-lfs is installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/datasets/MIRA-SJTU/STI-Bench

```


## Dataset Description

STI-Bench evaluates MLLMs’ spatial-temporal understanding by testing their ability to estimate, predict, and understand object appearance, pose, displacement, and motion from video data. The benchmark contains more than 2,000 question-answer pairs across 300 videos, sourced from real-world environments such as desktop settings, indoor scenes, and outdoor scenarios. These videos are taken from datasets like Omni6DPose, ScanNet, and Waymo.

STI-Bench is designed to challenge models on both static and dynamic spatial-temporal tasks, including:

| Task Name | Description |
| :-------- | :---------- |
| **3D Video Grounding** | Locate the 3D bounding box of objects in the video |
| **Ego-Centric Orientation** | Estimate the camera's rotation angle |
| **Pose Estimation** | Determine the camera pose |
| **Dimensional Measurement** | Measure the length of objects |
| **Displacement & Path Length** | Estimate the distance traveled by objects or camera |
| **Speed & Acceleration** | Predict the speed and acceleration of moving objects or camera |
| **Spatial Relation** | Identify the relative positions of objects |
| **Trajectory Description** | Summarize the trajectory of moving objects or camera|

### Dataset Fields Explanation

The dataset contains the following fields, each with its respective description:

| Field Name        | Description |
| :---------------- | :---------- |
| **Video**         | The string corresponding to the video file. |
| **Source**        | The string corresponding to the video source, which can be "ScanNet," "Waymo," or "Omni6DPose." |
| **Task**          | The string representing the task type, e.g., "3D Video Grounding," "Ego-Centric Orientation," etc. |
| **QType**         | The string specifying the question type, typically a multiple-choice question. |
| **Question**      | The string containing the question presented to the model. |
| **Prompt**        | Additional information that might be helpful for answering the question, such as object descriptions. |
| **time_start**    | A float64 value indicating the start time of the question in the video (in seconds). |
| **time_end**      | A float64 value indicating the end time of the question in the video (in seconds). |
| **Candidates**    | A dictionary containing answer choices in the format `{"A": "value", "B": "value", ...}`. |
| **Answer**        | The string corresponding to the correct answer, represented by the choice label (e.g., "A", "B", etc.). |
| **Answer Detail** | A string representing the precise value or description of the correct answer. |
| **ID**            | A sequential ID for each question, unique within that video. |
| **Scene**         | The string describing the scene type of the video, such as "indoor," "outdoor," or "desktop." |

## Evaluation

STI-Bench evaluates performance using accuracy, calculated based on exact matches for multiple-choice questions. 

We provide an out-of-the-box evaluation of STI-Bench in our [GitHub repository](https://github.com/MIRA-SJTU/STI-Bench)

## Citation

```bibtex
@article{li2025sti,
    title={STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?}, 
    author={Yun Li and Yiming Zhang and Tao Lin and XiangRui Liu and Wenxiao Cai and Zheng Liu and Bo Zhao},
    year={2025},
    journal={arXiv preprint arXiv:2503.23765},
}
```