Datasets:

AI-YesLab
/

3DVerifierBench

pretty_name: 3DVerifierBench
language:
  - en
license: other
license_name: 3dverifierbench-mixed-scope
license_link: >-
  https://huggingface.co/datasets/AI-YesLab/3DVerifierBench/blob/main/licenses/README.md
task_categories:
  - visual-question-answering
size_categories:
  - 1K<n<10K
tags:
  - 3d
  - verification
  - benchmark
configs:
  - config_name: benchmark
    default: true
    data_files:
      - split: test
        path: metadata/benchmark.jsonl

3DVerifierBench

Grounding Generative Verification in 3D Content
Shuanglin Li, Jiaying Lin, Shuquan Ye.

Paper: arXiv:2610.22360

The test set contains 2,581 samples across ten tasks, with 970 True and 1,611 False answers. It includes the original image/video inputs and available 3D assets. Related samples are grouped into 1,167 cases.

Test annotations

metadata/benchmark.jsonl contains one sample per line, with eight fields:

Field	Meaning
`sample_id`	Short neutral ID, such as `object_0001_01`
`task_id`	Task name: `object`, `part`, `material`, `part_grounding`, `part_relation`, `affordance_region`, `grasp`, `identity`, `fine_geometry` or `motion`
`question`	The factual claim; for Motion, the complete caption-based instruction
`answer`	Ground-truth Boolean judgment
`reference_explanation`	Reference rejection explanation used for scoring
`instruction`	Static-task instruction; Motion's instruction is already in `question`
`input_type`	Original image/video input protocol
`evidence`	Ordered image/video entries with paths, media IDs and content hashes

IDs follow <task>_<case>_<sample>, matching the evidence directory. For example, grasp_0090_01 uses evidence/grasp/case_0090/sample_01/. Sample numbers use a fixed neutral ordering and do not encode the answer. Each sample is evaluated independently. All paths are relative to the dataset root.

metadata/inputs.jsonl is the same public input projection without answers or reference explanations. Object, Part and Material tasks use the question and eight ordered views; their generic task instruction is omitted from model input, following the paper. metadata/motion_frames16_inputs.jsonl supplies the 134 alternative sixteen-frame Motion inputs with the original sampling and layout information.

Evaluation grouping, sample construction type, source attribution and geometry availability are in metadata/sample_metadata.jsonl. The exact source/candidate 3D asset associations are in metadata/sample_asset_links.jsonl. Join these indexes by the same short sample_id; asset filenames have independent numbering. The code release's reader joins the indexes automatically. Original questions, answers, reference explanations, media bytes and evidence order are preserved.

Directory layout

3DVerifierBench/
├── README.md
├── metadata/
│   ├── benchmark.jsonl
│   ├── inputs.jsonl
│   ├── motion_frames16_inputs.jsonl
│   ├── sample_metadata.jsonl
│   ├── sample_asset_links.jsonl
│   ├── grasp_poses.jsonl
│   └── dataset_stats.json
├── evidence/
│   └── <task>/case_0001/
│       ├── source.glb
│       ├── candidate_01.glb
│       └── sample_01/
│           ├── view_01.png
│           └── ...
├── manifests/
│   ├── benchmark_manifest.json
│   ├── pair_links.jsonl
│   └── triplet_links.jsonl
└── licenses/

The tasks are Object, Part, Material, Part Grounding, Part Relation, Affordance Region, Grasp Validity, Identity Preservation, Fine-grained Geometry and Motion.

Images, videos and 3D assets

Object, Part and Material tasks have eight ordered images, view_01.png through view_08.png.
Region, relation, affordance and Grasp use views.png, annotated_global.png and annotated_detail.png.
Identity and FineGeo use pair.png.
Motion has video.mp4, the alternative frames16.png, and original OBJ frames under mesh_sequence/frame_00001.obj, with captions.txt.
Identity assets include A/B OBJ and PLY meshes, source views and synthesis data.
FineGeo includes supplied candidate GLBs and official reference_cad.ply files. metadata/fine_geometry_models_info.json records their upstream CAD metadata.

There are 11,769 image/video input files, representing 7,681 unique original contents, plus 12,420 source asset files and 200 complete Grasp scene GLBs. The 134 Motion sequences contain 6,088 original OBJ frames.

Every Grasp sample contains scene.glb with its unchanged object, palm and both fingers at the recorded candidate pose. Its case-level source.glb remains the object alone. metadata/grasp_poses.jsonl retains the 4x4 source transform and gripper dimensions in meters. The complete scene applies the same canonicalization to object and gripper, then converts source Z-up to glTF Y-up. Rebuild the scenes from the code checkout with:

python scripts/export_grasp_scenes.py --benchmark-root /path/to/3DVerifierBench

Forty DB-3DME samples have original turntable GIFs instead of source meshes. Thirty FineGeo samples have official reference CADs without an original candidate mesh. Their geometry_status is recorded in metadata/sample_metadata.jsonl. Provided images/videos remain authoritative for reproducing the paper's visual input protocol; new renders may have different cameras and pixels.

Load

from datasets import load_dataset

test = load_dataset("AI-YesLab/3DVerifierBench", split="test")
print(test[0]["sample_id"], test[0]["question"], test[0]["answer"])

Download the complete repository to resolve evidence and 3D asset paths locally:

from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download(repo_id="AI-YesLab/3DVerifierBench", repo_type="dataset"))
sample = test[0]
views = [root / item["path"] for item in sample["evidence"]]

For an already extracted ZIP, use the local JSON reader:

test = load_dataset("json", data_files={"test": str(root / "metadata/benchmark.jsonl")}, split="test")

Install the reader with python -m pip install datasets. Source meshes, labels, reference explanations and related-sample metadata are excluded from subject prompts. The separate 3DVerifier code release handles prompt construction, model execution and scoring.

Evaluate

From the code checkout, configure your own model endpoint and credentials:

python scripts/prepare_manifest.py --benchmark-root /path/to/3DVerifierBench --task part --output work/part_manifest
python scripts/run_evaluation.py --manifest work/part_manifest --config examples/direct.json --output runs/part
python scripts/score_run.py --run runs/part

For native Motion, prepare its video manifest with scripts/prepare_motion_manifest.py and use scripts/evaluate_motion_video.py. Preserve the model's original native-video or sixteen-frame protocol for paper reproduction.

Indexes, license and citation

manifests/benchmark_manifest.json records paths, membership and content hashes. metadata/dataset_stats.json provides global and per-task counts. The linked indexes contain 661 specification pairs, 727 candidate pairs and 615 triplets. The benchmark's 1,123 recorded source groups are disjoint from the training resource's 4,011 groups; this check does not cover unrecorded semantic duplicates.

Original annotations are CC BY 4.0. Upstream media, meshes, captions and native annotations retain their source-specific terms. See licenses and attribution and retain the supplied notices. Training data and model weights are separate distributions.

Please cite the arXiv preprint:

@misc{li2026groundinggenerativeverification3d,
  title={Grounding Generative Verification in 3D Content},
  author={Shuanglin Li and Jiaying Lin and Shuquan Ye},
  year={2026},
  eprint={2610.22360},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2610.22360}
}