Infinite-World

Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory

Ruiqi Wu^1,2,3*, Xuanhua He^4,2*, Meng Cheng^2*, Tianyu Yang², Yong Zhang^2‡, Chunle Guo^1,3†, Chongyi Li^1,3, Ming-Ming Cheng^1,3

¹Nankai University   ²Meituan   ³NKIARI   ⁴HKUST

^*Equal Contribution   ^†Corresponding Author   ^‡Project Leader

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Highlights

Infinite-World is a robust interactive world model with:

• Real-World Training — Trained on real-world videos without requiring perfect pose annotations or synthetic data
• 1000+ Frame Memory — Maintains coherent visual memory over 1000+ frames via Hierarchical Pose-free Memory Compressor (HPMC)
• Robust Action Control — Uncertainty-aware action labeling ensures accurate action-response learning from noisy trajectories

Installation

Environment: Python 3.10, CUDA 12.4 recommended.

1. Create conda environment

conda create -n infworld python=3.10
conda activate infworld

2. Install PyTorch with CUDA 12.4

Install from the official PyTorch index (no local whl):

pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124

3. Install Python dependencies

pip install -r requirements.txt

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Checkpoint Configuration

All model paths are configured in configs/infworld_config.yaml. Paths are relative to the project root unless absolute.

Download checkpoints

Download from Wan-AI/Wan2.1-T2V-1.3B and place files under checkpoints/:

File / directory	Config key	Description
models/Wan2.1_VAE.pth	vae_cfg.vae_pth	VAE weights
models/models_t5_umt5-xxl-enc-bf16.pth	text_encoder_cfg.checkpoint_path	T5 text encoder
models/google/umt5-xxl (folder)	text_encoder_cfg.tokenizer_path	T5 tokenizer
infinite_world_model.ckpt	checkpoint_path	DiT model weights

• DiT checkpoint: Can be downloaded from TBD.

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Upload to Hugging Face (including checkpoints)

To upload this repo to Hugging Face Hub (code + checkpoints/):

1. Login

   pip install huggingface_hub
   huggingface-cli login
   

   Use a token from https://huggingface.co/settings/tokens (need write permission).

2. Upload From the project root (infinite-world/):

   python scripts/upload_to_hf.py YOUR_USERNAME/infinite-world
   

   Or set the repo and run:

   export HF_REPO_ID=YOUR_USERNAME/infinite-world
   python scripts/upload_to_hf.py
   

   The script uploads the whole directory (including checkpoints/) and skips __pycache__, outputs, .git, etc. Large checkpoint files are uploaded via the Hub API; the first run may take a while depending on size and network.

3. Create repo manually (optional)
   You can create the model repo first at https://huggingface.co/new (type: Model), then run the script with that repo_id.

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Results

Quantitative Comparison

Model	Mot. Smo.↑	Dyn. Deg.↑	Aes. Qual.↑	Img. Qual.↑	Avg. Score↑	Memory↓	Fidelity↓	Action↓	ELO Rating↑
Hunyuan-GameCraft	0.9855	0.9896	0.5380	0.6010	0.7785	2.67	2.49	2.56	1311
Matrix-Game 2.0	0.9788	1.0000	0.5267	0.7215	0.8068	2.98	2.91	1.78	1432
Yume 1.5	0.9861	0.9896	0.5840	0.6969	0.8141	2.43	1.91	2.47	1495
HY-World-1.5	0.9905	1.0000	0.5280	0.6611	0.7949	2.59	2.78	1.50	1542
Infinite-World	0.9876	1.0000	0.5440	0.7159	0.8119	1.92	1.67	1.54	1719

Citation

If you find this work useful, please consider citing:

@article{wu2026infiniteworld,
  title={Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory},
  author={Wu, Ruiqi and He, Xuanhua e.a.},
  journal={arXiv preprint arXiv:2602.02393},
  year={2026}
}

License

This project is released under the MIT License.