README.md

---
language:
- en
license: apache-2.0
task_categories:
  - text-to-3d
  - image-feature-extraction
  - zero-shot-classification
tags:
  - 3d-generation
  - text-to-3d
  - game-assets
  - safety
  - risk
  - safety-labels
  - content-safety
  - genre-taxonomy
  - genre-conditioned
  - autoregressive
  - mesh
  - point-cloud
  - 3d-gaussian-splatting
  - multi-view
  - dino
  - clip
size_categories:
- 1K<n<10K
pretty_name: GenSAGA-3D
---

# GenSAGA-3D

## Genre-Conditioned, Safety-Aware 3D Game Assets for Text-to-3D Research

**GenSAGA-3D** is an open research dataset designed to study a part of text-to-3D generation that is still poorly represented in existing benchmarks: **autoregressive 3D generation in structured game-asset space, with contextual safety labels and reusable multi-modal 3D representations**.

The dataset contains **9,307 text-conditioned triangle meshes** generated from **9,419 curated prompts**, organized over **14 game genres × 29 functional asset-type groups**, with **4 contextual safety tiers** ranging from unambiguously safe to adversarial. Each asset is sanity-tested and distributed with a consistent set of water-tight geometry, multi-view, point-based, signed-distance, and embedding representations.

### Why GenSAGA-3D?

Most existing text-to-3D evaluation datasets concentrate on diffusion-based generators and primarily organize samples around generic object categories or benchmark prompts. GenSAGA-3D is designed around four dimensions that are useful for studying the next generation of 3D foundation models:

1. **Autoregressive generation** — a public corpus built from an autoregressive text-to-3D generator rather than a per-scene optimization or diffusion-only pipeline.
2. **Genre-conditioned structure** — prompts are systematically organized by game genre and functional asset type, enabling controlled analysis across visual and gameplay contexts.
3. **Contextual safety** — four safety tiers explicitly distinguish ordinary content, content that is acceptable within a game genre, borderline/dual-use content, and adversarial content.
4. **Research-ready 3D representations** — every sample is accompanied by watertight geometry, SDF samples, a 3DGS-ready point cloud, calibrated multi-view renders, and pre-computed DINOv2/CLIP features.

Together, these properties make GenSAGA-3D useful not only as a generation benchmark, but also as a **shared resource for 3D representation learning, multi-view understanding, safety modeling, retrieval, classification, and downstream 3D foundation-model research**.


---

## At a Glance

| Property | GenSAGA-3D |
|---|---:|
| **3D Assets** | **9,307 x 5** |
| Game genres | **14** |
| Functional asset groups | **29** |
| Genre × asset-type prompt cells | **113** |
| Complexity tiers | **3** (Basic / Refined / Complex) |
| Safety tiers | **4 (T0–T3)** |
| Watertight meshes | **100%** |
| 2-manifold meshes | **100%** |
| Multi-view renders | **18 / asset** |
| Render resolution | **800 × 800** |
| SDF samples | **200K / asset** |
| 3DGS-ready points | **100K / asset** |
| DINOv2 features | **18 × 1024 / asset** |
| CLIP features | **18 × 768 / asset** |
| Total storage | ~178 GB |
| License | **Apache 2.0** |

---

## Core Contributions

### 1. A public autoregressive text-to-3D corpus

GenSAGA-3D provides a large, openly released collection of **text-conditioned meshes produced by an autoregressive 3D generator**. This complements the predominantly diffusion-based landscape of public T2-3D evaluation resources and creates a concrete testbed for studying how autoregressive models behave across diverse prompts, object classes, genres, and safety contexts.

### 2. A structured genre × asset taxonomy

Instead of treating game objects as an unstructured collection of meshes, GenSAGA-3D defines a controlled taxonomy spanning **14 game genres** and **29 functional asset groups**, yielding **113 genre/asset-type prompt cells**.

This structure supports controlled studies such as:

- generation quality across genres;
- geometry/topology changes as a function of genre;
- performance across functional object categories;
- cross-genre retrieval and classification;
- systematic evaluation of semantic and stylistic coverage.

### 3. Contextual safety labels for 3D content

GenSAGA-3D uses a **four-tier safety scheme**:

| Tier | Meaning | Typical interpretation |
|---|---|---|
| **T0** | Unambiguously safe | Ordinary objects and benign game assets |
| **T1** | Safe-in-genre | Content appropriate in an explicitly fictional/game context |
| **T2** | Borderline / dual-use | Content whose risk depends on realism or context |
| **T3** | Adversarial | Explicitly harmful, provocative, or adversarial content |

The key design goal is **contextual safety rather than binary safe/unsafe labeling**. For example, a fantasy longsword may be appropriate as an RPG asset while a realistic weapon prompt in another context may require a different risk interpretation.

Safety tiers are assigned from the genre × asset-type construction rules; human validation is planned for a future release.

### 4. A multi-modal 3D research resource, not just a mesh dump

Every asset is packaged through a consistent four-layer representation stack:

- **Geometry:** watertight triangle mesh.
- **Implicit / point representations:** 100K surface samples + 100K volume samples with signed-distance values, plus a 100K-point 3DGS-ready cloud.
- **Multi-view supervision:** 18 calibrated 800×800 views containing RGBA, normals, depth, and camera-to-world matrices.
- **Semantic embeddings:** per-view DINOv2-Large and CLIP-ViT-L/14 features.

This allows researchers to use the same corpus for geometry, rendering, multi-view learning, semantic retrieval, classification, 3D Gaussian Splatting initialization, safety modeling, and multimodal representation research without rebuilding the preprocessing pipeline.

### 5. Strong geometric validity at release time

All **9,307 meshes are watertight and 2-manifold directly from the generator**, with no repair needed during preprocessing. 
This provides a clean geometry substrate for downstream research and removes a common confound between generation quality and mesh-repair heuristics.

---

## What Makes the Dataset Distinct?

GenSAGA-3D is intended to occupy the intersection:

```text
                 AUTOREGRESSIVE 3D GENERATION
                            │
                            │
               ┌────────────┼────────────┐
               │            │            │
               ▼            ▼            ▼
        GENRE STRUCTURE  SAFETY      MULTI-MODAL 3D
               │        CONTEXT        REPRESENTATIONS
               └────────────┼────────────┘
                            ▼
                      GenSAGA-3D
```

The resulting dataset is useful for questions such as:

- Does an autoregressive T2-3D generator generalize uniformly across game genres?
- How does geometric complexity vary with genre and functional asset type?
- Can 3D safety classifiers distinguish contextual game content from genuinely adversarial content?
- Can multi-view visual features predict semantic alignment or safety without requiring full mesh processing?
- How well do geometry, rendering, and embedding representations agree for the same generated asset?

---

## Key Statistics

| Metric | Value |
|---|---|
| Total assets | 9,307 |
| Unique prompts | 9,419 (112 duplicates across cells) |
| Generator | Cube3D v0.5 (autoregressive; Apache 2.0) |
| Genres | 14 (10 aesthetic + 4 gameplay-context) |
| Asset-type groups | 29 |
| Prompt cells (genre × asset type) | 113 |
| Complexity tiers | Basic (968) / Refined (4,339) / Complex (4,000) |
| Safety tiers | T0 (5,302) / T1 (2,617) / T2 (277) / T3 (1,111) |
| Watertight from generator | 100% |
| 2-manifold from generator | 100% |
| Mean vertices | 148,623 |
| Mean faces | 297,246 |
| Multi-view renders | 18 per asset, 800×800, 40° FOV |
| DINOv2 embedding | 18 × 1024 float32 per asset |
| CLIP embedding | 18 × 768 float32 per asset |
| Total storage | ~178 GB |
| License | Apache 2.0 |

---

## Dataset Structure

```text
GenSAGA-3D/
├── README.md
├── metadata.parquet          # One row per asset; complete metadata
├── combined_all.csv          # Human-readable CSV
├── combined_G{1..14}.csv     # Per-group CSVs
└── data/
    ├── 3dgs/                 # {asset_name}_3dgs.ply
    ├── wt/                   # {asset_name}_wt.npz
    ├── pts/                  # {asset_name}_pts.npz
    ├── views/                # {asset_name}_views.npz
    └── embed/                # {asset_name}_embed.npz
```

### Selective download

The dataset is designed for **modality-specific research**. File sizes range from ~118 KB to tens of MB per asset. Use `metadata.parquet` to filter the corpus and download only the artifact families required for your experiment.

```python
from huggingface_hub import snapshot_download

REPO = "YOUR_USERNAME/GenSAGA-3D"

# Metadata + pre-computed embeddings
snapshot_download(
    repo_id=REPO,
    repo_type="dataset",
    allow_patterns=["metadata.parquet", "combined_all.csv", "data/embed/*"],
)

# Watertight meshes only
snapshot_download(
    repo_id=REPO,
    repo_type="dataset",
    allow_patterns=["metadata.parquet", "data/wt/*"],
)
```

---

## Metadata Schema

`metadata.parquet` / `combined_all.csv` contains 83 columns per asset.

### Prompt and taxonomy

| Column | Type | Description |
|---|---|---|
| `prompt_hash` | str | SHA-256 hash of the prompt text |
| `asset_name` | str | Unique asset identifier, e.g. `G1A1_0` |
| `csv_row` | int | Original prompt-table row |
| `cell_id` | str | Genre × asset-type prompt cell |
| `genre` | str | One of 14 game genres |
| `asset_group` | str | One of 29 functional asset groups |
| `sub_type` | str | Specific object subtype |
| `material` | str | Material descriptor |
| `condition` | str | Condition descriptor |
| `size_tier` | str | `small` / `standard-sized` / `large` / `colossal` |
| `complexity_tier` | str | `Basic` / `Refined` / `Complex` |
| `safety_tier` | str | `T0` / `T1` / `T2` / `T3` |
| `allocation_tier` | str | Generation-planning allocation category |
| `novel_flag` | bool | Whether the prompt was marked as novel |
| `prompt_text` | str | Full text prompt supplied to the generator |

### Generation and geometry

| Column | Type | Description |
|---|---|---|
| `n_verts_gen` | int | Vertex count from the generator |
| `n_faces_gen` | int | Face count from the generator |
| `latency_s` | float | Generation latency in seconds |
| `status_render` | str | Generation/rendering status |

The repair/topology fields record both pre- and post-processing measurements. For this release, `tier_used` is `none` for all assets because no mesh repair was required.

### Multi-view and embeddings

| Column | Type | Description |
|---|---|---|
| `n_views` | int | 18 |
| `image_size` | int | 800 |
| `camera_distance` | float | 2.2 |
| `camera_fov_deg` | float | 40.0 |
| `status_embed` | str | Embedding generation status |
| `path_3dgs_ply` | str | 3DGS point-cloud path |
| `path_wt_npz` | str | Watertight mesh path |
| `path_pts_npz` | str | SDF/sample path |
| `path_views_npz` | str | Multi-view render path |
| `path_embed_npz` | str | Embedding path |

---

## Artifact Formats

### `_3dgs.ply` — 3D Gaussian Splatting-ready point cloud

Binary little-endian PLY with 100,000 points and nine per-point properties:

| Property | Type | Description |
|---|---|---|
| `x`, `y`, `z` | float32 | Normalized position |
| `nx`, `ny`, `nz` | float32 | Barycentric-interpolated normal |
| `red`, `green`, `blue` | uint8 | Placeholder color `(128, 128, 128)` |

### `_wt.npz` — watertight triangle mesh

```text
vertices            (V, 3) float32
faces               (F, 3) int32
shift               (3,)   float32
scale               ()     float32
tier_used           ()     string
resolution_used     ()     int64
```

Meshes are normalized to a common coordinate convention.

### `_pts.npz` — surface and volume samples

```text
surface_points      (100K, 3) float32
surface_normals     (100K, 3) float32
surface_face_index  (100K,)   int32
surface_barycentric (100K, 3) float32
volume_points       (100K, 3) float32
volume_sdf          (100K,)   float32
```

### `_views.npz` — calibrated multi-view supervision

```text
rgba                (18, 800, 800, 4) uint8
normal              (18, 800, 800, 3) uint8
depth               (18, 800, 800) float32
camera_to_world     (18, 4, 4) float32
camera_angle_x      () float32
```

The 18 views are rendered from a Fibonacci-sphere camera configuration.

### `_embed.npz` — frozen semantic features

```text
dino_embeddings     (18, 1024) float32   # DINOv2-Large
clip_embeddings     (18, 768)  float32   # CLIP-ViT-L/14
```

---

## Safety Tier Definitions

| Tier | Name | Description | Count |
|---|---|---|---:|
| **T0** | Unambiguously Safe | Benign objects with no intended harmful interpretation | 5,302 (57.0%) |
| **T1** | Safe-in-Genre | Potentially harmful-looking objects whose meaning is acceptable within the explicit game/genre context | 2,617 (28.1%) |
| **T2** | Borderline / Dual-Use | Objects whose interpretation depends on realism, intent, or context | 277 (3.0%) |
| **T3** | Adversarial | Explicitly harmful, provocative, or adversarial content | 1,111 (11.9%) |

Safety tiers are derived from the dataset's genre × asset-type cross-mapping rules. The rule basis is documented in the dataset construction methodology. **Human validation is planned for v2.**

---

## Genre Taxonomy

### 10 aesthetic genres

| ID | Genre | Example content |
|---|---|---|
| G1 | Fantasy / Medieval | Swords, shields, spell tomes, treasure |
| G2 | Sci-Fi / Space | Plasma weapons, spaceships, control panels |
| G3 | Horror / Gothic | Broken weapons, skeletons, coffins, medical instruments |
| G4 | Steampunk / Dieselpunk | Brass firearms, clock towers, steam engines |
| G5 | Cyberpunk / Neo-noir | Chrome katanas, neon storefronts, cybernetic implants |
| G6 | Eldritch / Cosmic Horror | Ritual totems, necronomicons, cosmic artifacts |
| G7 | Post-Apocalyptic | Pipe rifles, irradiated beasts, makeshift carts |
| G8 | Mecha / Robotics | Mech swords, armor plates, hover tanks |
| G9 | Anime / Stylized | Spirit staffs, mascot creatures, cosplay outfits |
| G10 | Modern / Realistic | Shotguns, headphones, IV stands, tennis rackets |

### 4 gameplay-context genres

| ID | Genre | Example content |
|---|---|---|
| G11 | RPG / Adventure | Treasure keys, scrolls, remedy vials, relics |
| G12 | Combat / FPS | Assault rifles, plate carriers, balaclavas |
| G13 | Survival | Ration packs, snare traps, bandages, makeshift carts |
| G14 | Roleplay / Social | Bookshelves, pizza, fruit bowls, guitars, hats |

---

## Example Uses

### Safety-conditioned analysis

```python
import pandas as pd

df = pd.read_parquet("metadata.parquet")

# Adversarial subset
T3 = df[df["safety_tier"] == "T3"]

# Safe-in-genre subset
T1 = df[df["safety_tier"] == "T1"]

print(len(T3), len(T1))
```

### Genre-conditioned analysis

```python
fantasy_melee = df[
    (df["genre"] == "Fantasy / Medieval") &
    (df["asset_group"] == "Weapons - Melee")
]

print(f"Fantasy melee assets: {len(fantasy_melee)}")
```

### Load a watertight mesh

```python
from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    repo_id="YOUR_USERNAME/GenSAGA-3D",
    filename="data/wt/G1A1_0_wt.npz",
    repo_type="dataset",
)

mesh = np.load(path)
print(mesh["vertices"].shape, mesh["faces"].shape)
```

---

## Research Impact

GenSAGA-3D is intended as a **shared experimental substrate** rather than a single-purpose benchmark. Its combination of structured prompts, safety labels, validated geometry, calibrated multi-view observations, and frozen visual embeddings enables several research directions:

- **Autoregressive T2-3D evaluation:** compare generation behavior across genres, asset types, and complexity levels.
- **3D safety:** train and evaluate geometry-, image-, and multimodal safety classifiers.
- **Context-aware safety:** study the difference between ordinary, safe-in-genre, borderline, and adversarial content.
- **Multi-view representation learning:** use consistent 18-view observations with camera calibration.
- **3D retrieval and classification:** exploit meshes, renderings, DINOv2 features, CLIP features, or combinations.
- **Geometry analysis:** investigate topology, complexity, manifoldness, and genre-dependent geometric statistics.
- **3DGS and implicit representations:** directly consume the released point clouds and SDF samples.
- **Efficient downstream experimentation:** use the pre-computed representations without regenerating meshes or running GPU-heavy embedding pipelines.

### Why this matters

A useful 3D benchmark should make it possible to separate **generation quality**, **semantic coverage**, **contextual safety**, and **representation quality** rather than conflating them in a single score. GenSAGA-3D is designed around that separation.

---

## Positioning Relative to Existing 3D Resources

GenSAGA-3D is complementary to established 3D resources such as ShapeNet and Objaverse, which are primarily general-purpose repositories, and to T2-3D evaluation benchmarks that focus on generated-quality assessment.

Its distinguishing design choices are:

| Dimension | GenSAGA-3D |
|---|---|
| Generation paradigm | **Autoregressive text-to-3D** |
| Prompt organization | **Genre × functional asset type** |
| Safety | **Four-tier contextual labels** |
| Geometry | **Watertight, 2-manifold meshes** |
| Multi-view supervision | **18 calibrated views** |
| Implicit samples | **200K surface/volume samples** |
| 3DGS representation | **100K-point PLY per asset** |
| Frozen visual features | **DINOv2 + CLIP** |
| Licensing | **Apache 2.0** |

The goal is not to replace general-purpose 3D repositories, but to provide a structured experimental setting for **generation, safety, and multimodal 3D analysis**.

---

## Limitations and Responsible Use

- The assets are **AI-generated**, not scans of real-world objects.
- The dataset reflects the characteristics and biases of the chosen autoregressive generator and the prompt taxonomy.
- Safety labels are rule-based in the current release; **human validation is planned for v2**.
- T1 explicitly represents contextual acceptability and should not be treated as universally safe outside the game-asset context.
- Some T3 examples contain potentially sensitive or adversarial content and should be handled according to the policies of the downstream application.

---

## Citation

```bibtex
@misc{gensaga3d2026,
  title        = {GenSAGA-3D: Genre-Conditioned, Safety-Aware 3D Game Assets for Text-to-3D Research},
  author       = {TBD},
  year         = {2026},
  howpublished = {Hugging Face Dataset},
  url          = {https://huggingface.co/datasets/YOUR_USERNAME/GenSAGA-3D}
}
```

---

## License

GenSAGA-3D is released under the **Apache License 2.0**. See [`LICENSE`](LICENSE) for details.

A part of the dataset has been generated using the open, autoregressive, text-to-3D model Cube3D v0.5, which is also released under Apache 2.0. Cube3D is referenced solely for generator provenance and reproducibility; GenSAGA-3D is an independent dataset release and is not affiliated with or endorsed by Roblox.

---

## Acknowledgments

We acknowledge the creators of the open components used in the dataset pipeline, including **Cube3D v0.5**, **DINOv2-Large**, and **CLIP-ViT-L/14**. These components are cited for reproducibility and technical provenance.