status string | validated int64 | unique_ids int64 | actions dict | splits dict |
|---|---|---|---|---|
PASS | 1,000 | 1,000 | {
"move_forward": 177,
"noop": 181,
"interact": 167,
"move_right": 159,
"move_backward": 187,
"move_left": 129
} | {
"train": 800,
"validation": 100,
"test": 100
} |
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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