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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**
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