| ---
|
| license: cc-by-nc-4.0
|
| pretty_name: GameVision-StateAction-1K
|
| size_categories:
|
| - 1K<n<10K
|
| tags:
|
| - synthetic-data
|
| - computer-vision
|
| - game-ai
|
| - world-models
|
| - embodied-ai
|
| - agents
|
| - segmentation
|
| - state-action
|
| - multimodal
|
| - simulation
|
| ---
|
|
|
| # GameVision-StateAction-1K
|
|
|
| A synthetic multimodal benchmark for visual state-action learning and game-agent research.
|
|
|
| ## Contents
|
|
|
| - 1,000 Blender-rendered RGB scenes
|
| - 1,000 binary target segmentation masks
|
| - exact target 3D state
|
| - target bounding boxes
|
| - camera pose and matrix
|
| - analytic camera-space depth
|
| - deterministic actions
|
| - deterministic next-state oracle
|
| - train / validation / test splits
|
|
|
| ## Modalities
|
|
|
| Each record links:
|
|
|
| `RGB ? object state ? action ? expected next state`
|
|
|
| with a paired target segmentation mask.
|
|
|
| ## Intended use
|
|
|
| - multimodal agent evaluation
|
| - game AI
|
| - embodied AI
|
| - visual state tracking
|
| - world-model experiments
|
| - action prediction
|
| - segmentation-conditioned planning
|
| - synthetic benchmark development
|
|
|
| ## Ground truth
|
|
|
| Ground truth is derived from the synthetic scene state and deterministic transition rules.
|
|
|
| No LLM determines the correct target state or action transition.
|
|
|
| ## Provenance
|
|
|
| The scenes are generated synthetically.
|
|
|
| - no copyrighted gameplay capture
|
| - no player recordings
|
| - no personally identifiable information
|
| - no third-party game assets required
|
|
|
| ## Validation
|
|
|
| The release is checked for:
|
|
|
| - file existence
|
| - unique IDs
|
| - action validity
|
| - bbox structure
|
| - transition consistency
|
| - positive camera depth
|
| - positive camera distance
|
|
|
| ## Public release
|
|
|
| This repository is a public benchmark and demonstration release.
|
|
|
| Larger and domain-specific private variants can be produced with custom:
|
|
|
| - action spaces
|
| - scene distributions
|
| - object families
|
| - camera configurations
|
| - task semantics
|
| - difficulty distributions
|
| - failure states
|
| - recovery trajectories
|
| - private evaluation splits
|
|
|
| ## Commercial data work
|
|
|
| RegalFire builds custom AI data and evaluation sets for:
|
|
|
| - agents
|
| - world models
|
| - VLMs
|
| - RAG
|
| - enterprise AI
|
| - multimodal systems
|
|
|
| Contact RegalFire for custom/private dataset generation.
|
|
|
| ## License
|
|
|
| CC BY-NC 4.0 for this public release.
|
|
|
| ## Custom / Private Dataset Work |
|
|
| RegalFire builds custom AI datasets, evaluation sets and data pipelines for: |
|
|
| - AI agents |
| - computer-use systems |
| - multimodal models |
| - world models |
| - RAG systems |
| - code agents |
| - enterprise AI |
|
|
| Available services include: |
|
|
| - synthetic data generation |
| - private evaluation datasets |
| - agent trajectories |
| - failure / recovery datasets |
| - multimodal RGB / segmentation / state-action data |
| - web data acquisition |
| - cleaning and deduplication |
| - structured dataset packaging |
| - continuous dataset production |
|
|
| For private or custom work: |
|
|
| **Email: ootiris@gmail.com** |
|
|
| Hugging Face: **RegalFire**
|
|
|
| --- |
|
|
| ## RegalFire — Custom / Private Dataset Work |
|
|
| RegalFire builds custom AI datasets, evaluation sets and data pipelines for: |
|
|
| - AI agents |
| - computer-use systems |
| - multimodal models |
| - world models |
| - RAG systems |
| - code agents |
| - enterprise AI |
|
|
| Available services include: |
|
|
| - synthetic data generation |
| - private evaluation datasets |
| - agent trajectories |
| - failure / recovery datasets |
| - multimodal RGB / segmentation / state-action data |
| - web data acquisition |
| - cleaning and deduplication |
| - structured dataset packaging |
| - continuous dataset production |
|
|
| For custom or private work: |
|
|
| **Email: ootiris@gmail.com** |
|
|
| Hugging Face: **RegalFire**
|
|
|
