Dataset card: add banner + spinning teaser GIF, JMD publication, structured sections

README.md

@@ -1,34 +1,155 @@
 ---
 license: cc-by-nc-4.0
 pretty_name: DeepWheel
+size_categories:
+  - 1K<n<10K
+task_categories:
+  - tabular-regression
+  - image-to-3d
 tags:
   - engineering-design
-  - 3d
+  - automotive
+  - wheel
   - wheel-design
+  - 3d
+  - mesh
   - cad
   - depth-estimation
+  - surrogate-modeling
+  - modal-analysis
+---
+
+<div align="center">
+  <img src="assets/banner.png" alt="DeepWheel — generated 3D wheel designs" width="100%">
+</div>
+
+<h1 align="center">DeepWheel</h1>
+<p align="center"><b>Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation</b></p>
+
+<p align="center">
+  <a href="https://doi.org/10.1115/1.4069899"><img src="https://img.shields.io/badge/J.%20Mech.%20Des.-10.1115%2F1.4069899-2ea44f.svg" alt="Journal of Mechanical Design"></a>
+  <a href="https://arxiv.org/abs/2504.11347"><img src="https://img.shields.io/badge/arXiv-2504.11347-b31b1b.svg" alt="arXiv"></a>
+  <img src="https://img.shields.io/badge/license-CC--BY--NC%204.0-blue.svg" alt="License: CC-BY-NC 4.0">
+</p>
+
+<p align="center"><b>Published in ASME Journal of Mechanical Design</b> (2026) 148(5): 051702 &nbsp;·&nbsp; KAIST SmartDesignLab / Narnia Labs</p>
+
+---
+
+## Contents
+- [Overview](#overview)
+- [The data, qualitatively](#the-data-qualitatively)
+- [Dataset structure](#dataset-structure)
+- [Usage](#usage)
+- [Applications](#applications)
+- [Citation](#citation)
+- [License](#license)
+
 ---
 
-# DeepWheel
+## Overview
 
-Hugging Face distribution of the **DeepWheel** dataset (KAIST Smart Design Lab / Narnia Labs),
-a multi-modal dataset of generatively designed wheels: rendered images, predicted depth maps,
-reconstructed 3D meshes, 3D CAD models, and simulation results.
+> **DeepWheel** is a **synthetic automotive wheel** dataset for **design and performance evaluation**,
+> generated with a generative-AI framework: 2D renders are synthesized with **Stable Diffusion**, lifted to
+> 3D via **2.5D depth estimation + reconstruction**, and **structurally simulated** to extract engineering
+> performance. It provides **6,000+ photo-realistic images** and **900+ structurally-analyzed 3D models**,
+> each as multi-view images, depth maps, a reconstructed surface mesh, and a parametric CAD model, paired with
+> **performance labels** (mass and modal natural frequencies) — coupling **3D geometry ↔ performance** for
+> data-driven design, surrogate modelling, and image-to-3D research.
 
-## Components
+<div align="center">
+  <img src="assets/teaser.gif" alt="A generated wheel spinning about its axle" width="42%">
+  <br><sub>A reconstructed wheel design, spinning about its axle.</sub>
+</div>
 
-| File | Contents |
+| | |
 |---|---|
-| `1_rendered_images.zip` | Rendered images |
-| `2_predicted_depth_maps.zip` | Predicted depth maps |
-| `3_3D_recon_meshes.zip` | Reconstructed 3D meshes |
-| `4_3D_cad_models.zip` | 3D CAD models |
-| `deepwheel_sim_results.csv` | Simulation results |
-| `readme.docx` | Official dataset documentation (incl. file-matching instructions) |
+| **Domain** | automotive wheels (rims) |
+| **Scale** | 6,000+ rendered images · 900+ structurally-analyzed 3D models |
+| **Modalities** | rendered images · depth maps · 3D meshes · CAD models |
+| **Performance labels** | mass · modal frequencies (Mode 7, Mode 11) |
+| **Paper** | *J. Mech. Des.* (2026) 148(5):051702 · [doi:10.1115/1.4069899](https://doi.org/10.1115/1.4069899) · [arXiv:2504.11347](https://arxiv.org/abs/2504.11347) |
+
+---
+
+## The data, qualitatively
+
+<div align="center">
+  <img src="assets/banner.png" alt="Variety of generated wheel designs" width="100%">
+  <br><sub><b>Generated wheel-design variety</b> — reconstructed 3D rim meshes spanning a range of spoke geometries.</sub>
+</div>
+
+---
+
+## Dataset structure
+
+```
+DeepWheel/
+├── 1_rendered_images.zip      # multi-view renders of each design
+├── 2_predicted_depth_maps.zip # predicted depth maps
+├── 3_3D_recon_meshes.zip      # reconstructed surface meshes (.stl)
+├── 4_3D_cad_models.zip        # parametric CAD models
+├── deepwheel_sim_results.csv  # performance labels (per design)
+└── readme.docx                # official documentation (incl. file-matching instructions)
+```
+
+**Modalities**
+
+| Modality | File | Content |
+|---|---|---|
+| Rendered images | `1_rendered_images/…` | multi-view RGB renders |
+| Depth maps | `2_predicted_depth_maps/…` | predicted per-view depth |
+| 3D meshes | `3_3D_recon_meshes/*.stl` | reconstructed surface meshes |
+| CAD models | `4_3D_cad_models/…` | parametric CAD geometry |
+| Performance labels | `deepwheel_sim_results.csv` | `file_name, Mass, Mode7 Freq, Mode11 Freq` |
+
+---
+
+## Usage
+
+```bash
+huggingface-cli download KAIST-SmartDesignLab/DeepWheel --repo-type dataset --local-dir DeepWheel
+cd DeepWheel && for f in *.zip; do unzip -q "$f"; done
+```
+
+```python
+import pandas as pd, trimesh
+labels = pd.read_csv("deepwheel_sim_results.csv").set_index("file_name")
+case   = labels.index[0]
+mesh   = trimesh.load(f"stl/{case}.stl")          # reconstructed wheel
+mass, f7, f11 = labels.loc[case, ["Mass", "Mode7 Freq", "Mode11 Freq"]]
+```
+
+---
+
+## Applications
+
+- **Surrogate modelling** — predict mass and modal frequencies from 3D geometry or rendered views.
+- **Image-to-3D** — reconstruct wheel geometry from images / depth (paired renders + meshes + CAD).
+- **Generative & inverse design** — generate manufacturable wheel rims with target performance.
+- **Multimodal learning** — images ↔ depth ↔ mesh ↔ CAD ↔ performance.
+
+---
+
+## Citation
+
+```bibtex
+@article{deepwheel2026,
+  title   = {DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation},
+  journal = {Journal of Mechanical Design},
+  volume  = {148},
+  number  = {5},
+  pages   = {051702},
+  year    = {2026},
+  doi     = {10.1115/1.4069899}
+}
+```
+
+---
 
 ## License
 
-**CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International).
-Use, modification, and redistribution are permitted for **non-commercial** purposes with
-attribution. Commercial use of the dataset or derivative models is prohibited.
-See `readme.docx`.
+Released under **CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International). Use,
+modification, and redistribution are permitted for **non-commercial** purposes with attribution;
+commercial use of the dataset or derivative models is prohibited. See `readme.docx` and the paper
+([doi:10.1115/1.4069899](https://doi.org/10.1115/1.4069899)) for details. DeepWheel — KAIST Smart Design Lab / Narnia Labs.