StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation

StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation

Zijun Lin^1,2,4, Zeqing Wang^1,3, Cheston Tan⁴, Bihan Wen², Yeying Jin^1,3

¹Tencent, ²Nanyang Technological University, ³National University of Singapore, ⁴A*STAR

StatePlay: Beyond pixel-level realism toward mechanics-consistent Game World Models! Instead of modeling gameplay through visual observations alone, we explicitly predict internal game states and use them to guide frame generation, ensuring consistency with the underlying game mechanics.

Requirements: Python 3.10+, CUDA, and a GPU with bfloat16 support.

git clone https://github.com/Jimntu/StatePlay.git
cd StatePlay
python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -e .

Expected inference layout:

StatePlay/
├── examples/checkpoint/StatePlay.safetensors
└── base_model/Wan-AI/
    ├── Wan2.2-TI2V-5B/
    │   ├── Wan2.2_VAE.pth
    │   └── models_t5_umt5-xxl-enc-bf16.pth
    └── Wan2.1-T2V-1.3B/google/umt5-xxl/
        └── ... tokenizer files ...

To keep weights elsewhere:

export STATEPLAY_BASE_MODEL=/absolute/path/to/base_model
export STATEPLAY_CHECKPOINT=/absolute/path/to/StatePlay.safetensors

STATEPLAY_BASE_MODEL must directly contain Wan-AI/.

StatePlay uses separate visual and state transformer branches with shared joint attention. The visual branch predicts video latents; the state branch predicts the five normalized game states. Both branches are conditioned on the initial frame, text prompt, and action sequence.

StatePlay/
├── stateplay/
│   ├── pipeline.py             # public inference pipeline
│   ├── cli.py                  # command-line inference
│   └── models/
│       ├── dit.py              # StatePlay visual/state DiT
│       ├── vae.py              # video VAE wrapper
│       └── text_encoder.py     # text encoder wrapper
├── training/
│   ├── train.py                # training entry point
│   ├── runner_with_state.py    # optimization/checkpoint loop
│   └── data/                   # SF3 action, state, and prompt loading
├── diffsynth/                  # minimal Wan/StatePlay dependencies
├── scripts/
│   ├── inference.sh
│   ├── run_examples.sh
│   └── train.sh
└── examples/
    ├── inputs/                 # eight bundled inputs
    ├── checkpoint/             # local checkpoint
    └── generated/              # generated videos and state predictions

Generate all eight bundled examples:

export CUDA_VISIBLE_DEVICES=0
./scripts/run_examples.sh

Generate selected examples:

./scripts/run_examples.sh --only 01 03

Outputs are written to examples/generated/. Each example produces an MP4 and a _state.txt file. The model is loaded once for the entire run.

export CUDA_VISIBLE_DEVICES=0

./scripts/inference.sh \
  --image examples/inputs/01_macro_success_clip/first_frame.png \
  --actions examples/inputs/01_macro_success_clip/actions.parquet \
  --prompt-file examples/inputs/01_macro_success_clip/prompt.txt \
  --output output.mp4

This writes output.mp4 and output_state.txt. Defaults are 101 frames, 30 denoising steps, text CFG 5.0, state/action CFG 1.0, and seed 2.

Use custom model paths through STATEPLAY_BASE_MODEL and STATEPLAY_CHECKPOINT, or inspect all options with:

./scripts/inference.sh --help

Download the StatePlay dataset and the Wan2.2 DiT initialization weights listed in the model table above.

Run training:

export STATEPLAY_BASE_MODEL="$PWD/base_model"
export STATEPLAY_DATA_ROOT="$PWD/data/StatePlay-Dataset/SF3"
export STATEPLAY_OUTPUT="$PWD/outputs/StatePlay"
export CUDA_VISIBLE_DEVICES=0,1,2,3

./scripts/train.sh

The script derives the process count from CUDA_VISIBLE_DEVICES. It trains at 480×832 with 101 frames, learning rate 5e-5, state sampling end, and saves every 500 steps.

✉️ Feel free to email me (zijun002@e.ntu.edu.sg) or raise the issue if you have any questions about our work.

If you find our work useful in your research, please cite it as follows:

@misc{lin2026stateplaystateawaregameworld,
      title={StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation}, 
      author={Zijun Lin and Zeqing Wang and Cheston Tan and Bihan Wen and Yeying Jin},
      year={2026},
      eprint={2607.26754},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.26754}, 
}