Add metadata and improve dataset card

This PR improves the dataset card by:
- Adding structured YAML metadata (task category, license, and language).
- Updating the README with the latest news (MotionBench was accepted to CVPR 2025).
- Providing clear links to the project page, paper, and GitHub repository.
- Organizing the content for better readability while keeping the original installation and usage instructions.

README.md

@@ -1,23 +1,36 @@
+---
+license: cc-by-nc-sa-4.0
+task_categories:
+- video-text-to-text
+language:
+- en
+tags:
+- video-understanding
+- motion-understanding
+- benchmarking
+- video-qa
+---
+
 # MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
 
 ![VideoQA](https://img.shields.io/badge/Task-VideoQA-red)
 ![Multi-Modal](https://img.shields.io/badge/Task-Multi--Modal-red)
 ![MotionBench](https://img.shields.io/badge/Dataset-MotionBench-blue)  
 
-<font size=5><div align='center' > [[🍎 Project Page](https://motion-bench.github.io/)] [[📖 arXiv Paper](https://arxiv.org/abs/2501.02955)] [[📊 Dataset](https://huggingface.co/datasets/THUDM/MotionBench)][[🏆 Leaderboard](https://motion-bench.github.io/#leaderboard)][[🏆 Huggingface Leaderboard](https://huggingface.co/spaces/THUDM/MotionBench)]  </div></font>
+<font size=5><div align='center' > [[🍎 Project Page](https://motion-bench.github.io/)] [[📖 arXiv Paper](https://arxiv.org/abs/2501.02955)] [[📊 Dataset](https://huggingface.co/datasets/THUDM/MotionBench)] [[💻 GitHub](https://github.com/THUDM/MotionBench)] [[🏆 Leaderboard](https://motion-bench.github.io/#leaderboard)] [[🏆 HF Leaderboard](https://huggingface.co/spaces/THUDM/MotionBench)]  </div></font>
 
 <p align="center">
-    <img src="./docs/image1.png" width="96%" height="50%">
+    <img src="https://raw.githubusercontent.com/THUDM/MotionBench/main/docs/image1.png" width="96%" height="50%">
 </p>
 
-
-MotionBench aims to guide and motivate the development of more capable video understanding models, emphasizing the importance of fine-grained motion comprehension.
+MotionBench is a comprehensive evaluation benchmark designed to assess the fine-grained motion comprehension of video understanding models. It evaluates models' motion-level perception through six primary categories of motion-oriented question types and includes data collected from diverse sources, ensuring a broad representation of real-world video content.
 
 ---
 
 ## 🔥 News
 
-* **`2025.01.06`** 🌟 We released MotionBench, a new benchmark for fine-grained motion comprehension!
+* **`2025.02.27`** 🎉🎉🎉 MotionBench is accepted by CVPR 2025!!
+* **`2025.01.06`** 🌟🌟🌟 We released MotionBench, a new benchmark for fine-grained motion comprehension!
 
 ## Introduction
 
@@ -26,59 +39,37 @@ In recent years, vision language models (VLMs) have made significant advancement
 ### Features
 
 1. **Core Capabilities**: Six core capabilities for fine-grained motion understanding, enabling the evaluation of motion-level perception.
-2. **Diverse Data**: MotionBench collects diverse video from the web, public datasets, and self-synthetic videos generated via Unity3, capturing a broad distribution of real-world
-application.
+2. **Diverse Data**: MotionBench collects diverse video from the web, public datasets, and self-synthetic videos generated via Unity3, capturing a broad distribution of real-world application.
 3. **High-Quality Annotations**: Reliable benchmark with meticulous human annotation and multi-stage quality control processes.
 
 <p align="center">
-    <img src="./docs/image2.png" width="50%" height="20%">
+    <img src="https://raw.githubusercontent.com/THUDM/MotionBench/main/docs/image2.png" width="50%" height="20%">
 </p>
 
 ## Dataset
 
 ### License
 
-Our dataset is under the CC-BY-NC-SA-4.0 license.
+Our dataset is under the **CC-BY-NC-SA-4.0** license.
 
-LVBench is only used for academic research. Commercial use in any form is prohibited. We do not own the copyright of any raw video files.
+MotionBench is only used for academic research. Commercial use in any form is prohibited. We do not own the copyright of any raw video files. If there is any infringement in MotionBench, please contact shiyu.huang@aminer.cn or directly raise an issue, and we will remove it immediately.
 
-If there is any infringement in MotionBench, please contact shiyu.huang@aminer.cn or directly raise an issue, and we will remove it immediately.
+### Download and Usage
 
-### Download
+To use the benchmark, you should download `video_info.meta.jsonl` from this repository.
 
-Install video2dataset first:
+Install `video2dataset` first:
 
 ```shell
 pip install video2dataset
 pip uninstall transformer-engine
 ```
 
-Then you should download `video_info.meta.jsonl` from [Huggingface](https://huggingface.co/datasets/THUDM/MotionBench) and
-put it in the `data` directory.
-
-Each entry in the `video_info.meta.jsonl` file contains a video sample. Some of the dataset has the ground truth answer (the DEV set) and some not (the TEST set). You could use the DEV set to optimize your dataset and upload the answer file to our [leaderboard](https://huggingface.co/spaces/THUDM/MotionBench) to see your model's performance.
-
 #### Caption dataset
-Part of our dataset are derived from our self-annotated detailed video caption. we additionally release a dataset of 5,000 videos with manually annotated fine-grained motion descriptions, which are annotated and double-checked together with the benchmark annotation process. Each video includes dynamic information descriptions with annotation density reaching 12.63 words per second, providing researchers with resources for further development and training to enhance video models’ motion-level comprehension capabilities. 
-
-#### Self-collected dataset
-We provide the download link for all self-collected data. 
-
+We release a dataset of 5,000 videos with manually annotated fine-grained motion descriptions, which are annotated and double-checked together with the benchmark annotation process. Each video includes dynamic information descriptions with annotation density reaching 12.63 words per second.
 
 #### Publically available dataset
-For publically available data. we do not provide the orginal video files. You could download them from the original repo:
-```
-1. MedVid: https://github.com/deepaknlp/MedVidQACL
-2. SportsSloMo: https://cvlab.cse.msu.edu/project-svw.html
-3. HA-ViD: https://iai-hrc.github.io/ha-vid
-```
-After downloading the above mentioned dataset, find the mapping from the downloaded names to the filenames in our benchmark with the mapping file:
-```
-data/mapping.json
-```
-Then, cut the video to clips using the last two integers separated by `_`. 
-
-e.g., the video file `S10A13I22S1.mp4` is mapped to file `ef476626-3499-40c2-bbd6-5004223d1ada` according to the mapping file. To obtain the final test case `ef476626-3499-40c2-bbd6-5004223d1ada_58_59` in `video_info.meta.jsonl`, you should cut the video clip from `58` second to `59` second, yielding the final video sample for benchmarking.
+For publically available data (MedVid, SportsSloMo, HA-ViD), we do not provide the original video files. Please refer to the [GitHub repository](https://github.com/THUDM/MotionBench) for the mapping files and instructions on how to prepare the video clips.
 
 ## Install MotionBench
 
@@ -86,39 +77,16 @@ e.g., the video file `S10A13I22S1.mp4` is mapped to file `ef476626-3499-40c2-bbd
 pip install -e .
 ```
 
-## Get Evaluation Results and Submit to Leaderboard
+## Evaluation and Submission
 
-(Note: if you want to try the evaluation quickly, you can use the `scripts/construct_random_answers.py` to prepare a
-random answer file.)
+To prepare evaluation results and submit to the leaderboard:
 
 ```shell
 cd scripts
 python test_acc.py
 ```
 
-After the execution, you will get an evaluation results file `random_answers.json` in the `scripts` directory. You can submit the
-results to the [leaderboard](https://huggingface.co/spaces/THUDM/MotionBench).
-
-## 📈 Results
-
-- **Model Comparision:**
-
-<p align="center">
-    <img src="./docs/tab3.png" width="96%" height="50%">
-</p>
-
-- **Benchmark Comparison:**
-
-<p align="center">
-    <img src="./docs/image3.png" width="96%" height="50%">
-</p>
-
-
-- **Answer Distribution:**
-
-<p align="center">
-    <img src="./docs/image5.png" width="96%" height="50%">
-</p>
+After execution, you will get an evaluation results file `random_answers.json` in the `scripts` directory. You can submit the results to the [leaderboard](https://huggingface.co/spaces/THUDM/MotionBench).
 
 ## Citation