Update README.md

README.md

@@ -7,6 +7,55 @@ language:
 tags:
 - video
 - text
+- Robotics
+- Autonomous Driving
 size_categories:
 - 1K<n<10K
----
+---
+
+
+# Spatial-Temporal Intelligence Benchmark (STI-Bench)
+
+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
+
+
+```python
+from datasets import load_dataset
+sti_bench = load_dataset("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|
+
+## 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 VSI-Bench in our [GitHub repository](https://github.com/MIRA-SJTU/STI-Bench)
+
+## Citation
+
+```bibtex
+@article{li2025sti,
+    title={{Are MLLMs Ready for Precise Spatial-Temporal World Understanding?}},
+    author={Li, Yun and Zhang, Yiming and Lin, Tao and Liu, XiangRui and Cai, Wenxiao and Liu, Zheng and Zhao, Bo},
+    year={2025},
+    journal={arXiv preprint arXiv:2503.23765},
+}
+```