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README.md

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+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- text-to-3d
+- 3d-reconstruction
+- image-classification
+- zero-shot-classification
+tags:
+- 3d-generation
+- text-to-3d
+- game-assets
+- safety
+- genre-taxonomy
+- autoregressive
+- mesh
+- point-cloud
+- 3d-gaussian-splatting
+- multi-view
+- dino
+- clip
+- GenSAGA-3D
+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**.
+
+> **Dataset identity:** GenSAGA-3D is the contribution. The generator used to produce the meshes is a provenance detail, not the organizing principle of the dataset.
+
+---
+
+## 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 tier applied during preprocessing (`tier_used = none`). 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.