The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
index: struct<type_name: string, entity: string, label: string>
child 0, type_name: string
child 1, entity: string
child 2, label: string
part_level: struct<part_name: string, affordance: int64, graspable: bool, basic_description: string, functional_ (... 77 chars omitted)
child 0, part_name: string
child 1, affordance: int64
child 2, graspable: bool
child 3, basic_description: string
child 4, functional_description: string
child 5, movement_description: string
child 6, grasp_description: string
basic_info: struct<material: string, density: double, young: double, hardness: int64, poisson: double, friction: (... 8 chars omitted)
child 0, material: string
child 1, density: double
child 2, young: double
child 3, hardness: int64
child 4, poisson: double
child 5, friction: double
kinematic_info: struct<motion_types: list<item: string>, motion_info: struct<>>
child 0, motion_types: list<item: string>
child 0, item: string
child 1, motion_info: struct<>
category: string
object_name: string
volume: list<item: double>
child 0, item: double
mass: double
to
{'object_name': Value('string'), 'category': Value('string'), 'volume': List(Value('float64')), 'mass': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
index: struct<type_name: string, entity: string, label: string>
child 0, type_name: string
child 1, entity: string
child 2, label: string
part_level: struct<part_name: string, affordance: int64, graspable: bool, basic_description: string, functional_ (... 77 chars omitted)
child 0, part_name: string
child 1, affordance: int64
child 2, graspable: bool
child 3, basic_description: string
child 4, functional_description: string
child 5, movement_description: string
child 6, grasp_description: string
basic_info: struct<material: string, density: double, young: double, hardness: int64, poisson: double, friction: (... 8 chars omitted)
child 0, material: string
child 1, density: double
child 2, young: double
child 3, hardness: int64
child 4, poisson: double
child 5, friction: double
kinematic_info: struct<motion_types: list<item: string>, motion_info: struct<>>
child 0, motion_types: list<item: string>
child 0, item: string
child 1, motion_info: struct<>
category: string
object_name: string
volume: list<item: double>
child 0, item: double
mass: double
to
{'object_name': Value('string'), 'category': Value('string'), 'volume': List(Value('float64')), 'mass': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
UniPhys-Bench
UniPhys-Bench is a human-verified benchmark comprising 1,927 heterogeneous articulated 3D objects across two releases. It jointly evaluates articulation semantics, articulation structure, part-level intrinsic physical properties, and object-level scale and mass.
This repository is the primary UniPhys-Bench release. It contains 1,473 articulated 3D objects provided by Manycore Tech (群核科技), with part decompositions created by professional designers. The remaining 454 evaluation-only articulated 3D objects are released separately as UniPhys-Bench Part 2, completing the 1,927-object benchmark.
- Primary release (this repository): spatialverse/UniPhys-Bench
- Companion release (Part 2): breezexian/UniPhys-Bench-Part2
- Code: breezexian/UniPhysGen
- Paper: UniPhysGen: Unified Physical Grounding for Simulation-Ready 3D Assets
- Training dataset: breezexian/UniPhys-40K
Dataset summary
| Articulated 3D Objects | Parts | Motion-Relevant Components |
|---|---|---|
| 1,927 | ~16K | ~5.5K |
| 1,473 in this primary release + 454 in Part 2 |
Across both releases | Across both releases |
Dataset showcase
Each row follows one articulated object from its original textured mesh through part decomposition and affordance grounding to the motion of all articulated parts. In the affordance view, redder parts indicate higher interactivity.
| Original Mesh | Part Decomposition | Affordance | Articulated Motion |
|---|---|---|---|
| Raw textured geometry | Part-level segmentation | Redder = more interactive |
All articulated parts in motion |
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Simulation-based embodied interaction
1. Drawer Pulling
| Initial State → | Physical Contact → | Actuated State |
|---|---|---|
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2. Faucet Turning
| Initial State → | Physical Contact → | Actuated State |
|---|---|---|
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3. Laptop Closing
| Initial State → | Physical Contact → | Actuated State |
|---|---|---|
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Release scope
| Statistic | Value |
|---|---|
| Articulated objects in this repository | 1,473 |
| Entity ID range | UPB_00000454–UPB_00001926 |
| Role | Primary release |
The complete benchmark used in the paper contains 1,927 articulated 3D objects and is hosted as two independent releases:
| Release | Contents | Objects | Entity IDs |
|---|---|---|---|
| UniPhys-Bench | Manycore-provided assets | 1,473 | UPB_00000454–UPB_00001926 |
| UniPhys-Bench Part 2 | Evaluation-only assets held out from the UniPhys-40K source distribution | 454 | UPB_00000000–UPB_00000453 |
The two releases use the same annotation schema and evaluation protocol. They are hosted separately so that each repository can state the provenance and terms applicable to the assets it contains. Entity IDs are globally unique across the two releases and should not be renumbered.
The benchmark covers diverse object categories, structural complexities, part granularities, modeling styles, scales, masses, materials, and articulation patterns.
Benchmark construction
The assets in this repository were provided by Manycore Tech, and their part decompositions were created by professional designers. The UniPhys pipeline first produces articulation and physical-property annotations. Human annotators then inspect and correct joint type, axis, pivot, motion range, motion-dependent part groupings, and intrinsic physical-property plausibility for motion-relevant components.
UniPhys-Bench Part 2 provides the complementary source-distribution split used by the complete benchmark. It is distributed separately and is not contained in this repository.
Annotation scope
| Task | Inputs | Ground truth |
|---|---|---|
| Part-level intrinsic physical grounding | object and target-part geometry | part identity, semantic descriptions, material, density, Young's modulus, hardness, Poisson's ratio, friction, graspability, and affordance |
| Kinematic parameter grounding | object and target-part geometry | prismatic/revolute joint type, axis, pivot, and motion range |
| Articulation structure grounding | object and target-part geometry plus candidate-part metadata | IDs of parts that move together with the target part |
| Object-level physical grounding | complete-object geometry | object identity, category, dimensions, and mass |
Part-property units follow the paper: density is in g/cm^3, Young's modulus
in GPa, hardness in HV, and Poisson's ratio and friction are unitless.
Object dimensions are [L, W, H] in centimeters and mass is in kilograms.
Affordance is scored from 1 to 10, with smaller values indicating higher
affordance.
Motion labels use B for prismatic translation and C for revolute rotation.
The broader part-level annotations additionally use A for contact-only and
D for rigid or fixed parts.
Dataset structure
Each benchmark object is stored in one UPB_<id> directory. For example:
UPB_00000454/
├── annotations/
│ ├── object.json
│ ├── part_1.json
│ ├── part_2.json
│ └── ...
├── full_model/
│ ├── model.obj
│ ├── material.mtl
│ └── texture files
├── meta_data.json
├── model.urdf
├── parts/
│ ├── part_1.obj
│ ├── part_2.obj
│ ├── material and texture files
│ └── ...
└── plys/
├── model.ply
├── 1.ply
├── 2.ply
└── ...
| Path | Description |
|---|---|
parts/part_<id>.obj |
Decomposed part mesh, with its available MTL and texture assets. |
full_model/model.obj |
Complete object OBJ produced by concatenating all released part meshes. |
plys/model.ply |
Point cloud for the complete object. |
plys/<id>.ply |
Part point cloud aligned with parts/part_<id>.obj. |
annotations/object.json |
Object identity, category, real-world dimensions, and mass. |
annotations/part_<id>.json |
Part semantics, intrinsic physical properties, articulation parameters, and dependency group. |
meta_data.json |
Entity provenance, object summary, geometry metadata, and annotation version. |
model.urdf |
Articulated assembly of all released parts using the annotated joints and motion parameters. |
The same numeric part ID is used by part_<id>.obj, <id>.ply, and
part_<id>.json, so geometry and annotations can be joined without an
additional mapping file.
Simulation-ready URDF
model.urdf assembles all parts and encodes the annotated joint types and
motion parameters. During export, the mesh is rescaled using the annotated
object dimensions so that the assembled asset has a real physical size. URDF
geometry uses meters, while annotations/object.json stores dimensions in
centimeters.
The released URDF provides the articulated geometry and kinematic assembly, but intrinsic physical properties are not written into the URDF. Density, friction, mass, and other physical values should be read from the JSON annotations and assigned as needed for the target simulator and experiment.
Metadata
meta_data.json stores the dataset origin and released entity summary. A
Manycore entity uses metadata of the following form:
{
"id": "UPB_00000454",
"source": [
{
"dataset": "UniPhys-Bench",
"organization": "Manycore Tech Inc.",
"license_ref": "UniPhys-Bench"
}
],
"object": {
"category": "Furniture/StorageFurniture",
"object_name": "Corner Display Cabinet with Drawer"
},
"geometry": {
"asset_type": "decomposed_parts",
"num_parts": 6,
"format": "obj"
},
"annotation": {
"version": "v1.0"
}
}
Part-level annotations
Each annotations/part_<id>.json contains the part description, intrinsic
physical properties, motion type, joint parameters, and motion dependency:
{
"index": {
"type_name": "default",
"entity": "UPB_00000000",
"label": "3"
},
"part_level": {
"part_name": "Front Seat Trim Bar",
"affordance": 2,
"graspable": false,
"basic_description": "Slim ABS trim piece at the front edge of the seat.",
"functional_description": "Covers a seam and provides a finished edge.",
"movement_description": "Revolute; attached to the seat base.",
"grasp_description": "Grasp the handle of the bar."
},
"basic_info": {
"material": "metal/Steel",
"density": 7.85,
"young": 200.0,
"hardness": 180.0,
"poisson": 0.3,
"friction": 0.45
},
"kinematic_info": {
"motion_types": ["C"],
"motion_info": {
"dependency": [3],
"C": {
"axis": [-1.0, 0.0, 0.0],
"pos": [-0.02277967, 0.38144422, -0.02326505],
"range": [0.0, 0.785],
"damping": 0.03
}
}
}
}
kinematic_info.motion_info.dependency lists the part IDs that move together.
For movable parts, B contains prismatic parameters and C contains revolute
parameters; axis, pos, range, and damping describe the corresponding
joint.
range is the [lower, upper] motion interval; for a revolute (C) joint,
both limits are rotation angles expressed in radians.
Object-level annotations
annotations/object.json contains the object identity and global physical
properties:
{
"object_name": "Example Object",
"category": "Furniture/StorageFurniture",
"volume": [70.0, 68.0, 110.0],
"mass": 15.5
}
volume is [length, width, height] in centimeters and mass is in
kilograms. The values above illustrate the schema; released files contain the
verified values for each entity.
Download and prepare
Download this primary release:
hf download \
spatialverse/UniPhys-Bench \
--repo-type dataset \
--local-dir data/UniPhys-Bench
Generate model-ready point clouds:
python pre_process/generate_npzs.py \
--data_root data/UniPhys-Bench \
--output_dir data/UniPhys-Bench-processed/npzs
Generate one inference manifest for each evaluation task:
python pre_process/generate_jsons_for_inference.py \
--data_root data/UniPhys-Bench \
--npz_dir data/UniPhys-Bench-processed/npzs \
--output_dir data/UniPhys-Bench-processed/manifests
The command writes:
manifests/
├── intrinsic_physics_part.json
├── intrinsic_physics_object.json
├── kinematic_parameters.json
└── articulation_structure.json
Each sample keeps its complete benchmark annotation. UniPhysGen inference
embeds that record as source_sample, which the evaluation package reads as
ground truth.
To reproduce results on the complete benchmark of 1,927 articulated objects, also download UniPhys-Bench Part 2 and evaluate both releases with the same protocol. Keep the original entity IDs when preparing a combined data root.
Evaluation protocol
The paper reports the following metrics:
| Category | Metrics |
|---|---|
| Kinematic parameters | joint-type accuracy, axis angular error in degrees, pivot-to-ground-truth-axis distance, and motion-range mIoU |
| Articulation structure | set mIoU and micro-F1 over motion-coupled part IDs |
| Material properties | material-category accuracy, density ALDE, and friction MAE |
| Object scale and mass | ALDE and MnRE for dimensions and mass |
| Affordance | MAE over the 1-10 affordance score |
Kinematic parameters are evaluated on ground-truth movable parts, separating
parameter estimation from movable-part identification. Axis directions a and
-a are treated as the same articulation axis. Pivot error is measured in a
shared AABB-normalized object frame as point-to-ground-truth-axis distance.
Motion ranges are canonicalized to unsigned intervals before computing IoU.
Run the four evaluators on prediction JSON files or directories of per-sample records:
python -m eval intrinsic_physics_part PREDICTIONS \
--output intrinsic_physics_part_metrics.json
python -m eval intrinsic_physics_object PREDICTIONS \
--output intrinsic_physics_object_metrics.json
python -m eval kinematic_parameters PREDICTIONS \
--output kinematic_parameters_metrics.json
python -m eval articulation_structure PREDICTIONS \
--output articulation_structure_metrics.json
See the UniPhysGen README for the complete inference workflow and checkpoint commands.
Intended use
UniPhys-Bench is intended for research evaluation of unified physical grounding, including articulation reasoning, physical-property estimation, simulation-ready asset construction, embodied AI, and robotics simulation. It is an evaluation benchmark and should not be mixed into UniPhysGen training or model-selection data.
Scope and usage considerations
- Human annotators verify and correct articulation annotations and assess physical-property plausibility to support consistent research evaluation. Physical values are reference estimates for the depicted objects.
- The benchmark cannot cover every object category, material, mechanism, part granularity, or mesh failure mode.
- Results can depend on geometric completeness, scale correctness, texture quality, and candidate part decomposition.
Licensing and provenance
The complete contents of this release—including assets, annotations, metadata, derived representations, and dataset organization—are licensed under CC BY-NC 4.0. Commercial use is not permitted. See LICENSE_UNIPHYS_BENCH for the release-specific license notice and attribution information.
Use the source field in each entity's meta_data.json to retain the supplied
organization and license reference. Retain all required attribution, license,
and modification notices when sharing the data.
Citation
@article{li2026uniphysgen,
title = {UniPhysGen: Unified Physical Grounding for Simulation-Ready 3D Assets},
author = {Li, Xian and Wei, Rong and Yang, Lujie and Huang, Haolin and Fang, Junyuan and Tang, Siliang and Xiao, Jun and Tang, Rui and Li, Juncheng},
journal = {arXiv preprint arXiv:2607.13586},
year = {2026}
}
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