---
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**