USDCraft

Geometrically Grounded Programmatic Modeling
of Articulated 3D Assets for Simulation

Chuanrui Zhang1,2,*Zaijia Yang2Duomin Wang1,†Lu Shi1Daquan Zhou3Ruihua Zhang1Ziwei Wang2

1NVIDIA2NTU3PKU

*This work was conducted during an internship at NVIDIA.†Project Leader.

Paper · arXiv
↓

Demo

Method

USDCraft method: image and geometry inputs, axial geometry encoding, mesh-native modeling toolkit, physical parameters, program authoring and iterative geometric rechecking, with articulated assets exported for simulation.
Generation

New articulated assets from text or images

Without any input mesh, USDCraft writes an asset program from a prompt or a reference image. Pretrained LLMs bring broad knowledge of everyday objects, so new categories need no task-specific training. Every result below is the compiled, articulated USD asset — drag to orbit; parts move through their joint limits.

Upright piano

Image → asset
Input image for Upright piano
Input image
USDCraftLoading 3D

Backyard playground set

Image → asset
Input image for Backyard playground set
Input image
USDCraftLoading 3D

Manual wheelchair

Text → asset
Prompt
Create a complete, functionally usable, simulator-ready manual wheelchair with two rotating drive wheels, swiveling front casters, fold-up footrests, push rims, brakes, seat, backrest, and armrests.
USDCraftLoading 3D

Parcel locker bank

Text + image → asset
Input image for Parcel locker bank
Input image
Prompt
Create a complete, functionally usable, simulator-ready parcel locker bank with eight differently sized independently hinged doors, handles or electronic latches, kiosk screen, stable enclosure, and clear retrieval access.
USDCraftLoading 3D

Deployable satellite antenna

Text → asset
Prompt
Create a complete, functionally articulated, simulator-ready deployable satellite high-gain antenna assembly with a spacecraft mounting pedestal, two-axis azimuth-and-elevation gimbal, a parabolic reflector divided into a rigid center dish and two foldable side petals, locking hinge housings, central feed horn, support struts, cable-routing brackets, hard stops, and launch-restraint latches. The antenna must fold into a compact stowed configuration and deploy into a coherent dish shape, while the deployed reflector can aim through both gimbal axes without colliding with the pedestal.
USDCraftLoading 3D

Manual pallet jack

Text → asset
Prompt
Create a complete, functionally usable, simulator-ready manual pallet jack with pivoting steering handle, rotating steer and load wheels, lifting fork mechanism, control lever, and clear pallet entry.
USDCraftLoading 3D

Explore the generated assets

16 categories · pick an asset, scrub its joints, download the USDZ

Drag to orbit · scroll to zoom
Input image credits
Reconstruction

Rebuilding real objects for simulation

For real-to-sim, USDCraft rebuilds each source mesh as a program, separating fused parts and completing missing ones. The input can be a static asset, a raw phone scan, or a single image lifted to a mesh.

Source meshLoading 3D
USDCraft–AstraLoading 3D

Views are linked — orbit either side to compare the source with the rebuilt, articulated asset.

Comparison with prior methods

Same Hunyuan3D 2.1 input for every method · paper results (USDCraft and Mini Workflow: round 1)

Loading 3DInput image of the ergonomic office chair

Input · image + Hunyuan3D 2.1 mesh

No complete articulated outputpart geometry missing

ArtLLM

Loading 3D

SIMART3 parts · 2 joints

Loading 3D

Particulate9 parts · 8 joints

Loading 3D

Mini Workflow–Astra12 parts · 11 joints

Loading 3D

USDCraft–Astra15 parts · 14 joints

All six views share one camera — part colors show how each method splits the object into rigid parts; joints sweep through their limits.

Simulation

Loads into Isaac Sim, ready to run

Every compiled asset carries collision geometry, masses and joint limits, so it runs in Isaac Sim (PhysX) without manual fixes. Each clip shows the static asset, its dynamic articulation, and its collision geometry.

Warehouse forklift
Compact excavator
Baby grand piano
Robotic workbench arm
Real2Sim2Real

Policies trained only in simulation, deployed on real objects

We scan three real objects, rebuild them with USDCraft, collect 200 demonstrations per task in simulation, and train a Diffusion Policy that is deployed on the physical robot with no real-world data.

Open drawer

Simulation · data collection
Real world · policy rollout (4× speed)

Press toaster lever

Simulation · data collection
Real world · policy rollout (4× speed)

Turn on toaster

Simulation · policy rollout
Real world · policy rollout (4× speed)

Success rate across asset methods

Paper Table · 20 trials per setting · ↓ sim-to-real drop · I/M: image/mesh input

Asset methodInputOpen drawerPress toaster leverTurn on toaster
SimRealSimRealSimReal
Articraft–SolI90%20%↓7090%10%↓8080%20%↓60
ParticulateM0%0%50%10%↓400%0%
Mini Workflow–AstraI+M100%0%↓10090%60%↓3085%70%↓15
USDCraft–AstraI+M100%90%↓1090%85%↓585%85%↓0

BibTeX

@article{zhang2026usdcraft,
  title={USDCraft: Geometrically Grounded Programmatic Modeling of Articulated 3D Assets for Simulation},
  author={Zhang, Chuanrui and Yang, Zaijia and Wang, Duomin and Shi, Lu and Zhou, Daquan and Zhang, Ruihua and Wang, Ziwei},
  journal={arXiv preprint arXiv:2610.11322},
  year={2026}
}