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Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
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 match

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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.

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 Affordance color scale from less to more interaction
Redder = more interactive
All articulated parts in motion
Example 1 original mesh Example 1 part decomposition Example 1 affordance visualization Example 1 articulated motion
Example 2 original mesh Example 2 part decomposition Example 2 affordance visualization Example 2 articulated motion
Example 3 original mesh Example 3 part decomposition Example 3 affordance visualization Example 3 articulated motion

Simulation-based embodied interaction

1. Drawer Pulling

Initial State → Physical Contact → Actuated State
Drawer pulling initial state Drawer pulling physical contact Drawer pulling actuated state

2. Faucet Turning

Initial State → Physical Contact → Actuated State
Faucet turning initial state Faucet turning physical contact Faucet turning actuated state

3. Laptop Closing

Initial State → Physical Contact → Actuated State
Laptop closing initial state Laptop closing physical contact Laptop closing actuated state

Release scope

Statistic Value
Articulated objects in this repository 1,473
Entity ID range UPB_00000454UPB_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_00000454UPB_00001926
UniPhys-Bench Part 2 Evaluation-only assets held out from the UniPhys-40K source distribution 454 UPB_00000000UPB_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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