SCOPE: Simulating Cross-game Operations in Playable Environments for FPS World Models

Highlights

• Hybrid Action Space — Jointly processes continuous (4D dual-joystick) and discrete (6 binary buttons) control signals within a unified framework — the first FPS world model to do so.
• Dense Per-Frame Conditioning — Resolves overlapping actions at every single frame, enabling simultaneous multi-action composition (e.g., moving + aiming + firing) that reflects real gameplay complexity.
• Cross-Game Generalization — Trained on 7 diverse FPS titles, a single model generalizes zero-shot to entirely unseen game environments without fine-tuning.
• In-Scope / Out-of-Scope Decoupling — Spatially selective conditioning that separates localized in-scope effects (weapon recoil, HUD) from stable out-of-scope world generation — without any segmentation labels.

Model Overview

SCOPE is an interactive world model for first-person shooter (FPS) games. Built on Wan2.2-TI2V-5B, SCOPE inserts a conditioning module into each transformer block that reshapes features into per-pixel temporal sequences. Each spatial position computes its action response from local visual content, naturally separating in-scope effects (e.g., weapon firing, reloading) from out-of-scope world generation (e.g., stable surroundings) — without any segmentation labels.

Trained on CrossFPS (69K clips, 7 games, 10-DoF), SCOPE learns general visual-to-action mappings rather than game-specific patterns, enabling zero-shot transfer to unseen scenes.

Architecture

ActionModule Design

Each of the 30 DiT blocks contains an ActionModule with two conditioning paths:

• Mouse/Joystick Path: Sliding-window temporal features → MLP fusion → pixel-wise temporal self-attention with RoPE
• Keyboard/Button Path: Button embedding → temporal windowing → cross-attention (video queries, keyboard keys/values)

Both output projections are zero-initialized for stable residual training on top of frozen pretrained weights.

Generation Specs

Action Input Format

SCOPE accepts 10-DoF action inputs per frame via a Parquet file:

Controller Buttons (6D binary):

Dual Joystick (4D continuous):

Quick Start

Requirements

• Python >= 3.10
• PyTorch >= 2.0 with CUDA support
• GPU: NVIDIA with >= 24 GB VRAM (single GPU inference with CPU offload)

Installation

git clone https://github.com/z2tong/SCOPE.git
cd SCOPE
pip install -e .

Download Weights

# Download all weights (SCOPE DiT + Text Encoder + VAE + Tokenizer) in one command
huggingface-cli download zizhaotong/SCOPE --local-dir ./SCOPE

Inference

Single image + action sequence:

python inference.py \
    --model_dir ./SCOPE \
    --input_image input.png \
    --action_path action.parquet \
    --prompt "First-person shooter perspective in a modern city" \
    --seed 42

Batch processing (directory of images):

python inference.py \
    --model_dir ./SCOPE \
    --input_image_dir ./images \
    --action_path action.parquet \
    --prompt "First-person view in a battlefield" \
    --output_dir ./outputs

For full usage details and advanced options, see the GitHub repository.

Repository Contents

This repo contains all weights needed for inference in a single download:

Inference code is available at github.com/z2tong/SCOPE.

CrossFPS Dataset

SCOPE is trained on CrossFPS, the first multi-game FPS dataset with frame-aligned action telemetry:

Citation

@misc{scope2026,
      title={SCOPE: Simulating Cross-game Operations in Playable Environments for FPS World Models},
      author={Zizhao Tong and Hongfeng Lai and Zeqing Wang and Zhaohu Xing and Kexu Cheng and Haoran Xu and Zhao Pu and Shangwen Zhu and Ruili Feng and Jian Zhao and Yan Zhang and Hao Tang and Yeying Jin and Ling Shao},
      year={2026},
      eprint={2605.23345},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.23345}, 
}

Acknowledgements

We thank the Wan Team for open-sourcing Wan2.2 and the DiffSynth team for the inference framework.