README.md

metadata

license: cc-by-nc-4.0
pretty_name: PhysicalAI SimReady Assets
size_categories:
  - 1K<n<10K
tags:
  - simready
  - openusd
  - usd
  - usdz
  - isaac-sim
  - omniverse
  - robotics
  - embodied-ai
  - synthetic-data
  - 3d-assets
  - pbr-materials
  - hdri
  - digital-twin

PhysicalAI SimReady Assets

The constant half of a simulation-ready home. 764 SimReady object packages, 98 PBR surface packs and 13 environment maps — every asset needed to furnish, finish and light a synthetic interior, published once so scenes never have to carry their own copy.

A packaged SimReady home scene is typically 500 MB to 1.2 GB, and ~99% of that is these assets, embedded again in every single scene. Reference this dataset by URL instead and the same scene ships as a few megabytes — the geometry that is genuinely unique to it, plus links.

Measured on four apartment scenes, embedding versus linking the identical content:

scene	assets embedded	assets by URL
1-bedroom	502.0 MB	3.42 MB	147×
2-bedroom	664.7 MB	3.84 MB	173×
3-bedroom	700.4 MB	3.13 MB	224×
4-bedroom	1227.2 MB	3.78 MB	325×
total	3,094.3 MB	14.17 MB	218×

4-bedroom, assets embedded  ████████████████████████████████████████  1227.2 MB
4-bedroom, assets by URL    ▏                                            3.78 MB

Bigger scenes shrink more, because their extra size was almost entirely duplicated assets. The scenes are otherwise identical: same geometry, same poses, same physics — a remote build reproduces the embedded build's physics index exactly (117 referenced assets, 374 rigid bodies, 1,057 colliders).

Everything here is SimReady, not merely a mesh: each object carries collision geometry, mass and inertia, physics materials, semantic labels, and — where the object articulates — working joints. They are made to be simulated, not just rendered.

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What's inside

Path	Contents	Files	Size
simready_assets/	SimReady object packages (USD + textures + MDL + physics)	11,841	6,529 MB
materials/	PBR surface packs (floor, wall, ceiling, cabinet, countertop, tile, rug, carpet)	290	294 MB
hdri/	Equirectangular environment maps (day / evening / night pools)	13	83 MB
assets/	In-repo part kits (balcony railings)	42	4 MB

12,187 files, 6.9 GB total.

Objects — simready_assets/

764 packages spanning 76 semantic classes; 489 carry validated real-world dimensions in the catalog index. Each is an NVIDIA SimReady–format package, extracted intact:

simready_assets/132/
├── oven.usd                                      # root layer (USD crate)
├── OmniPBR.mdl                                   # material
├── SubUSDs/
│   └── textures/oven_display_diffuse.jpg         # …and siblings
├── gltf/pbr.mdl
├── .metadata/
│   ├── com.nvidia.simready.root_usds.json        # which layer is the root
│   └── com.nvidia.simready.packaging.bom.json    # vendor sha256 + blake3
└── com.nvidia.simready.packaging.json            # owner + license

All 76 object classes

appliance.cooktop, appliance.dishwasher, appliance.fridge, appliance.hood, appliance.microwave, appliance.oven, appliance.range, balcony_door, bathtub, bed, cabinet_base, cabinet_tall, cabinet_wall, cabinet_wall_microwave, ceiling_light, chair, clutter.appliance, clutter.book, clutter.bottle, clutter.cookware, clutter.cup, clutter.food, clutter.frame, clutter.lamp, clutter.mirror, clutter.plant, clutter.plate, clutter.small, clutter.toiletry, clutter.utensil, coffee_table, door, door_double, dresser, dustbin, faucet, fireplace, floor_lamp, floor_mirror, mirror, nightstand, ottoman, outdoor_chair, outdoor_table, plant, rug, shelf, shower, sink, socket, sofa, sofa_mod_armless, sofa_mod_chaise_left, sofa_mod_chaise_right, sofa_mod_corner, sofa_mod_end_left, sofa_mod_end_right, sofa_mod_loveseat_armless, sofa_mod_loveseat_left, sofa_mod_loveseat_right, sofa_mod_ottoman, stool, table, television, television_wall, toilet, toilet_paper, towel_bar, tv_unit, utensil_hanger, vanity, wall_art, wall_clock, wall_light, wardrobe, window

Appliances, seating, casework, bathroom and kitchen fixtures, lighting, wall decor, plants and small clutter — plus a modular sofa system (sofa_mod_*) whose corner, chaise, armless and loveseat pieces compose into arbitrary sectionals.

Surfaces — materials/

98 texture packs across 8 categories: cabinet, carpet, ceiling, countertop, floor, rugs, wall, wall_tile.

Packs follow the AmbientCG naming convention (*_Color, *_NormalGL / *_NormalDX, *_Roughness, *_AmbientOcclusion), so a loader can pick maps by suffix. The folder name encodes the physical tile size — floor/2_1.69_meter/ tiles at 1.69 m, which is what makes correct real-world UV scaling possible without per-pack metadata.

One exception worth knowing: under wall_tile/, the _N_meter number is repeats across the widest band (a cube_project count), not a physical size.

All maps are power-of-two. That is not cosmetic — non-power-of-two textures render blue or missing under Isaac Sim's RTX renderer while looking fine in Blender.

Lighting — hdri/

13 equirectangular environment maps in day / evening / night pools, at 1k and 2k. Intended as a UsdLuxDomeLight texture. Filenames are stable identities across resolutions, so a scene that picks an HDRI by name renders consistently whichever variant you resolve.

---

Two ways to use it

1. Download it, build against it locally

pip install huggingface_hub

# everything (6.9 GB)
hf download imagineio/PhysicalAI-SimReady-Assets --repo-type dataset --local-dir ./simready-assets

# or just what you need
hf download imagineio/PhysicalAI-SimReady-Assets --repo-type dataset --local-dir ./simready-assets \
    --include "simready_assets/132/*" "materials/floor/*" "hdri/2k/*"

from pxr import Usd, UsdGeom

stage = Usd.Stage.CreateNew("scene.usda")
oven = UsdGeom.Xform.Define(stage, "/World/Oven")
oven.GetPrim().GetReferences().AddReference("./simready-assets/simready_assets/132/oven.usd")
stage.GetRootLayer().Save()

2. Reference it by URL — no download

Every file is served at a stable, public URL:

https://huggingface.co/datasets/imagineio/PhysicalAI-SimReady-Assets/resolve/main/<path>

BASE = "https://huggingface.co/datasets/imagineio/PhysicalAI-SimReady-Assets/resolve/main"

oven.GetPrim().GetReferences().AddReference(f"{BASE}/simready_assets/132/oven.usd")
dome.CreateTextureFileAttr(f"{BASE}/hdri/2k/DayEnvironmentHDRI022_4K_HDR.exr")

⚠️ Your USD runtime must resolve https://

This is the one thing to check before committing to URL references.

Runtime	Resolver	https:// refs
Isaac Sim / Omniverse Kit	OmniUsdResolver (primary)	✅ resolved natively
usd-core from PyPI	ArDefaultResolver	❌ not a registered URI scheme

Stock usd-core does not merely fail to fetch — it treats the URL as a file path and collapses https:// to https:/ before giving up. Pipeline stages that must run offline (validation, CI, unit tests) need the downloaded copy from option 1.

Note also that a .usdz containing external references is outside the USDZ specification, which requires a package to be self-contained. Kit will open one; other tools may refuse. For a URL-referencing scene, prefer a plain .usda / .usdc.

Pin a revision for reproducibility. resolve/main follows the branch. Substituting a commit SHA freezes the bytes, which is what you want for a dataset artifact whose geometry was measured against a specific version of these assets:

https://huggingface.co/datasets/imagineio/PhysicalAI-SimReady-Assets/resolve/main/…            # follows main
.../resolve/<commit-sha>/…   # frozen

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Provenance and processing

Published by imagine.io from its SimReady asset catalog. The set is bounded and reproducible rather than open-ended: pinned_ids.json at the repo root lists exactly which object ids belong to it, so "did the set change?" is a question with an answer.

Each package retains the vendor's own sha256 + blake3 integrity BOM under .metadata/, so you can verify any file against what was published.

Two processing passes have been applied, both deliberate:

• Textures are capped at 2048 px albedo / 1024 px response maps. The uncapped set contained 8192×8192 normal maps on an egg boiler and two 81 MB diffuse PNGs on a single dining table — 5.89 GB of resolution that no renderer benefits from at furniture scale.
• Inverted collider windings are repaired. Inside-out SDF collider meshes make PhysX compute negative mass, which fails at attachShape rather than at load.

Pre-processing backups (*.srfp_orig) are not published — they are the bytes that were deliberately replaced.

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License

CC BY-NC 4.0 — Creative Commons Attribution-NonCommercial 4.0 International.

Free to use, modify and redistribute for non-commercial purposes with attribution. Every object package self-declares this license in its own com.nvidia.simready.packaging.json, and scenes built from these assets carry it forward in their packaging manifest.

For commercial licensing, contact imagine.io.

Citation

@misc{imagineio_physicalai_simready_assets,
  title        = {PhysicalAI SimReady Assets},
  author       = {imagine.io},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/imagineio/PhysicalAI-SimReady-Assets}},
  note         = {CC BY-NC 4.0}
}

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_{Generated by scripts/hf_assets.py card from the live asset roots — counts and sizes reflect revision main.}