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:
- Autoregressive generation — a public corpus built from an autoregressive text-to-3D generator rather than a per-scene optimization or diffusion-only pipeline.
- Genre-conditioned structure — prompts are systematically organized by game genre and functional asset type, enabling controlled analysis across visual and gameplay contexts.
- Contextual safety — four safety tiers explicitly distinguish ordinary content, content that is acceptable within a game genre, borderline/dual-use content, and adversarial content.
- 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:
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
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.
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
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
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
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
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
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
fantasy_melee = df[
(df["genre"] == "Fantasy / Medieval") &
(df["asset_group"] == "Weapons - Melee")
]
print(f"Fantasy melee assets: {len(fantasy_melee)}")
Load a watertight mesh
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
@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 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.