Add dataset card for SceneSmith preprocessed data

Hi, I'm Niels from the community science team at Hugging Face.

This PR adds a dataset card for the SceneSmith preprocessed data. It includes:
- Metadata for task categories (`robotics`) and the license (`mit`).
- Links to the paper, project page, and GitHub repository.
- A description of the preprocessed assets (ArtVIP and AmbientCG embeddings).
- Sample usage instructions for downloading the data using the Hugging Face CLI, as documented in the official repository.

README.md

@@ -1,93 +1,69 @@
 ---
-license: apache-2.0
+license: mit
+task_categories:
+- robotics
 tags:
-  - robotics
-  - simulation
-  - 3d-assets
-  - articulated-objects
-  - drake
-  - sdf
-pretty_name: SceneSmith Preprocessed Data
+- simulation
+- 3d
+- scene-generation
 ---
 
 # SceneSmith Preprocessed Data
 
-Preprocessed 3D assets for use with [SceneSmith](https://github.com/nepfaff/scenesmith), a VLM-agent-based system for generating physically realistic, interactive indoor scenes.
+This repository contains preprocessed assets and embeddings for the paper [SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes](https://huggingface.co/papers/2602.09153).
 
-## ArtVIP (Articulated Objects)
+[Project Page](https://scenesmith.github.io/) | [GitHub](https://github.com/nepfaff/scenesmith) | [arXiv](https://arxiv.org/abs/2602.09153)
 
-Simulation-ready articulated objects (cabinets, drawers, appliances, etc.) converted from the [ArtVIP](https://huggingface.co/datasets/x-humanoid-robomind/ArtVIP) dataset. Assets have been converted from USD to Drake SDFormat using [mesh-to-sim-asset](https://github.com/nepfaff/mesh-to-sim-asset), with:
+SceneSmith is a hierarchical agentic framework that generates simulation-ready indoor environments from natural language prompts. This dataset repository provides the preprocessed data required for the framework, including articulated objects and material embeddings.
 
-- **Drake SDFormat (.sdf)** model files with articulated joints
-- **Visual meshes** in glTF and OBJ formats
-- **Collision geometries** via convex decomposition (two variants available)
-- **Estimated physical properties** (mass, inertia per link)
-- **Pre-computed CLIP embeddings** for text-based retrieval
+## Dataset Description
 
-### Variants
+The artifacts in this repository include:
+- **ArtVIP Assets**: Preprocessed articulated furniture (cabinets, drawers, etc.) converted to Drake SDFormat with collision geometries and CLIP embeddings.
+- **AmbientCG Embeddings**: Pre-computed CLIP embeddings for free CC0 PBR materials from [AmbientCG](https://ambientcg.com/), used for semantic material retrieval during scene generation.
 
-| Archive | Collision Method | Description |
-|---------|-----------------|-------------|
-| `artvip/artvip_vhacd.tar.gz` | VHACD | Tighter collision geometries (recommended) |
-| `artvip/artvip_coacd.tar.gz` | CoACD | Can produce faster simulations |
+## Sample Usage
 
-Both variants include identical CLIP embeddings for retrieval.
+You can download specific parts of the dataset using the `huggingface-cli`.
 
-### Usage with SceneSmith
+### Download ArtVIP Assets
+To download the VHACD variant (recommended for tighter collision geometries):
 
-Download your preferred variant and extract to `data/artvip_sdf/`:
-
-```sh
-# Download VHACD variant (recommended)
+```bash
 huggingface-cli download nepfaff/scenesmith-preprocessed-data \
     artvip/artvip_vhacd.tar.gz --repo-type dataset --local-dir .
 mkdir -p data/artvip_sdf
 tar xzf artvip/artvip_vhacd.tar.gz -C data/artvip_sdf
-rm -rf artvip
+```
 
-# Or download CoACD variant
+Alternatively, to download the CoACD variant (which can produce faster simulations):
+```bash
 huggingface-cli download nepfaff/scenesmith-preprocessed-data \
     artvip/artvip_coacd.tar.gz --repo-type dataset --local-dir .
 mkdir -p data/artvip_sdf
 tar xzf artvip/artvip_coacd.tar.gz -C data/artvip_sdf
-rm -rf artvip
 ```
 
-### Asset Categories
-
-| Category | Description |
-|----------|-------------|
-| `large_furniture/` | Cupboards, wardrobes, etc. |
-| `small_furniture/` | Nightstands, side tables, etc. |
-| `major_appliances/` | Refrigerators, washing machines, etc. |
-| `small_appliances/` | Microwaves, toasters, etc. |
-| `household_items/` | Storage boxes, bins, etc. |
-
-### Manual Preprocessing
-
-To preprocess ArtVIP assets yourself (or to process updated versions), use [mesh-to-sim-asset](https://github.com/nepfaff/mesh-to-sim-asset) to convert from USD to Drake SDFormat.
-
-## AmbientCG Material Embeddings
+### Download AmbientCG Material Embeddings
 
-Pre-computed CLIP embeddings for [AmbientCG](https://ambientcg.com/) PBR materials, enabling text-based material retrieval for walls, floors, and surfaces.
-
-The embeddings are computed from AmbientCG preview images using CLIP (ViT-H-14-378-quickgelu, 1024D).
-
-> **Note:** You still need to download the actual material textures from AmbientCG separately (see SceneSmith README). This dataset only contains the pre-computed embeddings to skip the computation step.
-
-### Usage with SceneSmith
-
-```sh
+```bash
 huggingface-cli download nepfaff/scenesmith-preprocessed-data \
     --repo-type dataset \
     --include "ambientcg/embeddings/**" \
     --local-dir data/scenesmith-preprocessed-data
 mv data/scenesmith-preprocessed-data/ambientcg/embeddings data/materials/embeddings
-rm -rf data/scenesmith-preprocessed-data
 ```
 
-## Attribution
-
-The articulated object assets are derived from the [ArtVIP](https://huggingface.co/datasets/x-humanoid-robomind/ArtVIP) dataset, licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
-
-> Zhao Jin, Zhengping Che, Tao Li, Zhen Zhao, Kun Wu, Yuheng Zhang, Yinuo Zhao, Zehui Liu, Qiang Zhang, Xiaozhu Ju, Jing Tian, Yousong Xue, Jian Tang. *ArtVIP: Articulated Digital Assets of Visual Realism, Modular Interaction, and Physical Fidelity for Robot Learning.* 2025.
+## Citation
+
+```bibtex
+@misc{scenesmith2026,
+  title={SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes},
+  author={Nicholas Pfaff and Thomas Cohn and Sergey Zakharov and Rick Cory and Russ Tedrake},
+  year={2026},
+  eprint={2602.09153},
+  archivePrefix={arXiv},
+  primaryClass={cs.RO},
+  url={https://arxiv.org/abs/2602.09153},
+}
+```