Datasets:

raresense
/

nova3d-bench-preview

dataset_info:
  features:
    - name: item_id
      dtype: string
    - name: prompt
      dtype: string
    - name: domain
      dtype: string
    - name: level
      dtype: string
    - name: conditioning
      dtype: string
    - name: reference_image
      dtype: image
    - name: nova3d_preview
      dtype: image
    - name: nova3d_code
      dtype: string
    - name: meshy_preview
      dtype: image
    - name: trellis2_preview
      dtype: image
    - name: triposg_preview
      dtype: image
    - name: partcrafter_preview
      dtype: image
    - name: cubepart_preview
      dtype: image
    - name: meshanything_preview
      dtype: image
    - name: cadcoder_preview
      dtype: image
    - name: cadcoder_code
      dtype: string
    - name: blenderllm_preview
      dtype: image
    - name: blenderllm_code
      dtype: string
    - name: text2cad_preview
      dtype: image
    - name: text2cadquery_preview
      dtype: image
    - name: text2cadquery_code
      dtype: string
    - name: llamamesh_preview
      dtype: image
    - name: naive_blender_llm_preview
      dtype: image
    - name: naive_blender_llm_code
      dtype: string
  splits:
    - name: train
      num_bytes: 96231418
      num_examples: 54
  download_size: 95198118
  dataset_size: 96231418
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - text-to-3d
  - image-to-3d
pretty_name: Nova3D-Bench (preview)
size_categories:
  - n<1K

Nova3D-Bench (preview)

A frozen, spec-grounded benchmark from Nova3D: Code-Native Generation of Programmable 3D Assets: 54 prompts across six domains and three difficulty levels, each with a machine-checkable ground-truth spec. It compares code-native 3D generation — a program that produces the model, not just the model — against mesh-native, CAD, and part-structured baselines.

This is a preview release: one row per benchmark item, one column per pipeline. See Data completeness below — full .glb models are currently only available for 2 of the 13 pipelines; the rest are render previews only, pending baseline re-runs.

Dataset summary


Benchmark items	54
Domains	6 (architecture, characters, furniture, mechanical, tools, vehicles)
Difficulty levels	3 (L1 ≤8 parts / L2 9–25 parts / L3 >25 parts)
Conditioning	24 text-only, 30 image+text
Pipelines run	13 (Nova3D + 12 baselines)

Schema

One row per benchmark item. Metadata columns, then one <pipeline>_preview image column per pipeline, plus a <pipeline>_code text column for the 5 pipelines that emit source.

Column	Type	Description
`item_id`	string	e.g. `arch-L1-01`
`prompt`	string	Full instruction given to every system (image items still get a text prompt — see note below)
`domain`	string	One of the 6 domains
`level`	string	`L1` / `L2` / `L3`
`conditioning`	string	`text` or `image`
`reference_image`	image	Synthetic reference photo/sketch; a shared "n/a" placeholder for text-only items
`<pipeline>_preview`	image	Rendered textured view of that pipeline's output; "n/a" placeholder if the pipeline doesn't apply to this item's conditioning type, or the run failed
`<pipeline>_code`	string	Generated source (Nova3D `code.py`, CAD-Coder `model.py`, BlenderLLM `model.bpy.py`, Text2CADQuery `code.py`, naive-Blender-LLM `code.py`); `null` where not applicable

Note on reference_image/<pipeline>_preview: every Image column always holds a real image, never null — the Hugging Face Viewer renders null in an Image column as a broken "Not supported with pagination yet" cell instead of blank, so a shared dark "n/a" placeholder is used wherever nothing applies.

Note on prompts: image-conditioned items are still fully text-prompted — e.g. "Recreate the street bollard shown in the reference photo." — and any numeric constraints on an image item are spelled out in the prompt text too, since a constraint the system can't see in text isn't a fair test.

Pipeline registry — 13 total

Category	Pipelines	What it ships
Code-native	Nova3D, BlenderLLM	A program that produces the model
CAD	CAD-Coder, Text2CAD, Text2CADQuery	Parametric solids via a CAD kernel (CadQuery / STEP)
Part-structured	PartCrafter, CubePart	Mesh output with explicit part segmentation
Mesh-native	Meshy, TRELLIS.2, TripoSG, MeshAnything, LlamaMesh, naive-Blender-LLM*	Direct mesh surface, no source, no part structure

* naive-Blender-LLM is an ablation of Nova3D itself (same underlying LLM route, no master prompt / repair loop / validation pass) — included to isolate what Nova3D's engineering contributes, not a third-party system.

Coverage varies by pipeline because most baselines only support one conditioning type: Nova3D and Meshy ran on all 54 items; the 5 image-only baselines (TRELLIS.2, TripoSG, PartCrafter, CubePart, MeshAnything) only apply to the 30 image-conditioned items; the 5 text-only baselines (CAD-Coder, BlenderLLM, Text2CAD, Text2CADQuery, naive-Blender-LLM) only apply to the 24 text-conditioned items, and further drop below 24 from real generation failures on that baseline.

Data completeness

This preview currently ships render previews for every pipeline but full .glb models for only 2 of the 13 pipelines:

Status	Pipelines
`.glb` available	Nova3D (54/54), naive-Blender-LLM (39/54 — rest were failed exports)
`.glb` not currently recoverable	Meshy, TRELLIS.2, TripoSG, PartCrafter, CubePart, MeshAnything, CAD-Coder, Text2CAD, Text2CADQuery, LlamaMesh, BlenderLLM

Nova3D and naive-Blender-LLM route through persistent Azure Blob Storage, so their models were recoverable after the fact. The other 11 baselines were run against ephemeral Cloudflare Quick Tunnels or local/private-network servers at generation time; those endpoints no longer exist, so their .glbs are not recoverable from this repo and would require re-running the baseline or locating a separate archive of the original outputs.

License

CC BY 4.0. No named or trademarked objects are included in the benchmark (see IP policy in the paper's dataset section).