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 and Cheng, Meng and Yang, Tianyu and Zhang, Yong and Kang, Zhuoliang and Cai, Xunliang and Wei, Xiaoming and Guo, Chunle and Li, Chongyi and Cheng, Ming-Ming},
  journal={arXiv preprint arXiv:2602.02393},
  year={2026}
}

License

This project is released under the MIT License.