Upload README.md

README.md

@@ -143,41 +143,10 @@ homepage: https://vlar-group.github.io/PhysInOne.html
 <h1  align="center" style="font-size: 42px;">PhysInOne: Visual Physics Learning and Reasoning in One Suite</h1>
 
 <p align="center">
-  Siyuan Zhou<sup>1†</sup>  
-  Hejun Wang<sup>1†</sup>  
-  Hu Cheng<sup>1†</sup>  
-  Jinxi Li<sup>1†</sup>  
-  Dongsheng Wang<sup>1†</sup>  
-  Junwei Jiang<sup>1†</sup> <br>
-  Yixiao Jin<sup>1†</sup>  
-  Jiayue Huang<sup>1†</sup>  
-  Shiwei Mao<sup>1†</sup>  
-  Shangjia Liu<sup>2</sup>  
-  Yafei Yang<sup>1</sup> <br>
-  Hongkang Song<sup>1</sup>  
-  Shenxing Wei<sup>1</sup>  
-  Zihui Zhang<sup>1</sup>  
-  DataTeam<sup>1*</sup>  
-  Bing Wang<sup>2</sup>  
-  Zhihua Wang<sup>3</sup>  
-  Chuhang Zou<sup>4</sup>  
-  Bo Yang<sup>1‡</sup>
-</p>
-
-<p align="center">
-  <sup>1</sup> vLAR Group   
-  <sup>2</sup> The Hong Kong Polytechnic University   
-  <sup>3</sup> Syai Singapore   
-  <sup>4</sup> Meta
-</p>
-
-<p align="center">
-  <sup>†</sup> Equal Contribution   
-  <sup>‡</sup> Corresponding Author   
-</p>
-
-<p align="center">  
-  <sup>*</sup> {Peng Huang, Shijie Liu, Zhengli Hao, Hao Li, Yitian Li, Wenqi Zhou, Zhihan Zhao, Zongqi He, Hongtao Wen, Shouwang Huang, Peng Yun, Bowen Cheng, Pok Kazaf Fu, Wai Kit Lai, Jiahao Chen, Kaiyuan Wang, Zhixuan Sun, Ziqi Li, Haochen Hu, Di Zhang, Chun Ho Yuen}
+  vLAR Group |   
+  The Hong Kong Polytechnic University |   
+  Syai Singapore |
+  Meta
 </p>
 
 <p align="center">
@@ -210,26 +179,26 @@ homepage: https://vlar-group.github.io/PhysInOne.html
 <a href="#release-timetable">🚀 Release Timetable</a><br>
 <a href="#data-splits">📊 Data Splits</a><br>
 <a href="#how-to-download">📥 How to Download</a><br>
-&nbsp;&nbsp;&nbsp;<a href="#download-scripts">1. Download Scripts</a><br>
-&nbsp;&nbsp;&nbsp;<a href="#install-dependencies">2. Install Dependencies</a><br>
-&nbsp;&nbsp;&nbsp;<a href="#filter-cases">3. Filter Cases</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#filter-by-split">3.1 Split</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#filter-by-split-and-activity">3.2 Split + Activity</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#filter-by-phenomenon">3.3 Phenomenon</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="#random-sampling">3.4 Random Sampling</a><br>
-&nbsp;&nbsp;&nbsp;<a href="#download-selected-cases">4. Download Cases</a><br>
+   <a href="#download-scripts">1. Download Scripts</a><br>
+   <a href="#install-dependencies">2. Install Dependencies</a><br>
+   <a href="#filter-cases">3. Filter Cases</a><br>
+      <a href="#filter-by-split">3.1 Split</a><br>
+      <a href="#filter-by-split-and-activity">3.2 Split + Activity</a><br>
+      <a href="#filter-by-phenomenon">3.3 Phenomenon</a><br>
+      <a href="#random-sampling">3.4 Random Sampling</a><br>
+   <a href="#download-selected-cases">4. Download Cases</a><br>
 <a href="#dataset-structure">📦 Dataset Structure</a><br>
 <a href="#annotation-details">📝 Annotation Details</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#depth">Depth</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#segmentation">Segmentation</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#captions">Captions</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#trajectories">Trajectories</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#cameras">Cameras</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◦ <a href="#camera-json-structure">Camera JSON structure</a><br>
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;◦ <a href="#dynamic-camera-behavior">Dynamic camera behavior</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#point-clouds">Point Clouds</a><br>
+   • <a href="#depth">Depth</a><br>
+   • <a href="#segmentation">Segmentation</a><br>
+   • <a href="#captions">Captions</a><br>
+   • <a href="#trajectories">Trajectories</a><br>
+   • <a href="#cameras">Cameras</a><br>
+      ◦ <a href="#camera-json-structure">Camera JSON structure</a><br>
+      ◦ <a href="#dynamic-camera-behavior">Dynamic camera behavior</a><br>
+   • <a href="#point-clouds">Point Clouds</a><br>
 <a href="#data-fields">🧩 Data Fields</a><br>
-&nbsp;&nbsp;&nbsp;• <a href="#physical-phenomena">Physical Phenomena</a><br>
+   • <a href="#physical-phenomena">Physical Phenomena</a><br>
 <a href="#license">📜 License</a><br>
 <a href="#citation">📚 Citation</a><br>
 <a href="#contact">📮 Contact</a><br>
@@ -240,11 +209,9 @@ homepage: https://vlar-group.github.io/PhysInOne.html
 <h2 id="summary" style="margin-left: 0; font-size: 1.9em; line-height: 1.22; margin-top: 1.2em; margin-bottom: 0.5em;">📌 Summary</h2>
 <div style="margin-left: 24px;">
 
-**PhysInOne** is a large-scale synthetic dataset for visual physics learning and reasoning. It contains **153,810 dynamic 3D scenes** and **2 million annotated videos**, covering **71 basic physical phenomena** across four domains of everyday physics: **mechanics**, **optics**, **fluid dynamics**, and **magnetism**.
-
-Each scene may contain multi-object and multi-physics interactions in complex 3D environments. PhysInOne provides rich annotations including RGB videos, depth maps, object masks, 3D trajectories, camera poses, object meshes, material properties, and textual descriptions.
-
-The dataset is designed to support research on physics-aware video generation, future frame prediction, physical property estimation, motion transfer, physical reasoning, and world models.
+<p style="text-align: justify;">
+<strong>PhysInOne</strong> is a large-scale synthetic dataset for visual physics learning and reasoning, containing <strong>153,810 dynamic 3D scenes</strong> and <strong>2 million annotated videos</strong> across <strong>71 physical phenomena</strong> in mechanics, optics, fluid dynamics, and magnetism. Each scene features complex multi-object and multi-physics interactions with rich annotations, including RGB videos, depth maps, object masks, 3D trajectories, camera poses, object meshes, material properties, and textual descriptions. The dataset supports research on physics-aware video generation, future frame prediction, physical property estimation, motion transfer, physical reasoning, and world models.
+</p>
 
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