Upload README.md

README.md

@@ -207,11 +207,9 @@ homepage: https://vlar-group.github.io/PhysInOne.html
 <main class="physinone-main" style="flex: 1 1 500px; min-width: min(100%, 500px); line-height: 1.38; font-size: 15px;">
 
 <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;">
+<div style="margin-left: 24px; text-align: justify;">
 
-<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>
 
 <div class="physinone-summary-grid" style="display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 10px; margin: 18px 0;">
 
@@ -262,11 +260,9 @@ For large-scale downloading and filtering, please refer to the **How to Download
 </div>
 
 <h2 id="data-splits" style="margin-left: 0; font-size: 1.9em; line-height: 1.22; margin-top: 1.2em; margin-bottom: 0.5em;">📊 Data Splits</h2>
-<div style="margin-left: 24px;">
+<div style="margin-left: 24px; text-align: justify;">
 
-<p style="text-align: justify;">
 PhysInOne is divided into <strong>Train, Val, and Test</strong> splits, containing 122,988, 15,411, and 15,411 scenes, respectively. The three splits are generated with <strong>completely distinct 3D meshes and backgrounds</strong>, ensuring that no scene or visual content is shared across splits. This separation provides a robust benchmark for evaluating generalization and physical reasoning.
-</p>
 
 </div>
 
@@ -274,7 +270,7 @@ PhysInOne is divided into <strong>Train, Val, and Test</strong> splits, containi
 
 <h3 id="download-scripts" style="font-size: 1.35em; margin-top: 0.75em; margin-bottom: 0.22em; line-height: 1.2; margin-left: 24px;">1. Download Scripts</h3>
 
-<div style="margin-left: 40px;">
+<div style="margin-left: 40px; text-align: justify;">
 
 Please download the `assets` folder, which contains:
 
@@ -287,12 +283,10 @@ scripts/
   download.py
 ```
 
-<p style="text-align: justify;">
 PhysInOne is distributed across multiple Hugging Face dataset repositories because of its large scale. We provide two scripts for selecting and downloading cases:
 
-- `filter_cases.py`: parses the case-to-repository assignment file and exports selected cases to JSON.
-- `download.py`: downloads the selected case zip files from the corresponding Hugging Face shard repositories.
-</p>
+- `filter_cases.py`: exports selected cases to JSON.
+- `download.py`: downloads the selected case zip files from the corresponding repositories.
 
 </div>
 
@@ -308,9 +302,8 @@ pip install huggingface_hub tqdm
 
 <h3 id="filter-cases" style="font-size: 1.35em; margin-top: 0.75em; margin-bottom: 0.22em; line-height: 1.2; margin-left: 24px;">3. Filter Cases</h3>
 
-<div style="margin-left: 40px;">
+<div style="margin-left: 40px; text-align: justify;">
 
-<p style="text-align: justify;">
 Export a JSON file containing case information matching your filter criteria, for use in subsequent download scripts. The `filter_cases.py` script supports selection by:
 
 - Split: `train`, `val`, `test`
@@ -319,7 +312,6 @@ Export a JSON file containing case information matching your filter criteria, fo
 - Number of cases: globally sample `num` cases after filtering
 
 The following examples show common filtering workflows.
-</p>
 
 </div>
 
@@ -642,8 +634,6 @@ All 3D assets and materials included in PhysInOne have been sourced from publicl
 
 In total, assets comply with licenses including CC BY-NC, CC BY-SA, CC BY-NC-SA, CC0, CC BY, and RF, ensuring all files can be legally used for building a non-commercial dataset. Users must adhere to the original licenses for any redistribution or derivative work.  
 
-> ⚠️ Note: PhysInOne is intended for **non-commercial research and educational purposes**. For commercial use, users must verify the licensing terms of individual assets.
-
 </div>
 
 <h2 id="citation" style="margin-left: 0; font-size: 1.9em; line-height: 1.22; margin-top: 1.2em; margin-bottom: 0.5em;">📚 Citation</h2>
@@ -653,11 +643,14 @@ In total, assets comply with licenses including CC BY-NC, CC BY-SA, CC BY-NC-SA,
 If you use PhysInOne in your research, please cite:
 
 ```bibtex
-@article{zhou2026physinone,
-  title={PhysInOne: Visual Physics Learning and Reasoning in One Suite},
-  author={Zhou, Siyuan and Wang, Hejun and Cheng, Hu and Li, Jinxi and Wang, Dongsheng and Jiang, Junwei and Jin, Yixiao and Huang, Jiayue and Mao, Shiwei and Liu, Shangjia and others},
-  journal={arXiv preprint arXiv:2604.09415},
-  year={2026}
+@misc{zhou2026physinonevisualphysicslearning,
+      title={PhysInOne: Visual Physics Learning and Reasoning in One Suite}, 
+      author={Siyuan Zhou and Hejun Wang and Hu Cheng and Jinxi Li and Dongsheng Wang and Junwei Jiang and Yixiao Jin and Jiayue Huang and Shiwei Mao and Shangjia Liu and Yafei Yang and Hongkang Song and Shenxing Wei and Zihui Zhang and Peng Huang and Shijie Liu and Zhengli Hao and Hao Li and Yitian Li and Wenqi Zhou and Zhihan Zhao and Zongqi He and Hongtao Wen and Shouwang Huang and Peng Yun and Bowen Cheng and Pok Kazaf Fu and Wai Kit Lai and Jiahao Chen and Kaiyuan Wang and Zhixuan Sun and Ziqi Li and Haochen Hu and Di Zhang and Chun Ho Yuen and Bing Wang and Zhihua Wang and Chuhang Zou and Bo Yang},
+      year={2026},
+      eprint={2604.09415},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.09415}, 
 }
 ```