ReSim

The official implementation of our NeurIPS 2025 Spotlight paper:

ReSim: Reliable World Simulation for Autonomous Driving

Jiazhi Yang, Kashyap Chitta, Shenyuan Gao, Long Chen, Yuqian Shao, Xiaosong Jia, Hongyang Li, Andreas Geiger, Xiangyu Yue, Li Chen

Primary contact at Jiazhi Yang: jzyang@link.cuhk.edu.hk

Highlights

ReSim is a driving world model for reliable simulation of future ego-view driving videos under a wide range of ego behaviors.

• Reliable action control. ReSim supports action-conditioned future prediction for expert, free-driving, commanded, and hazardous non-expert behaviors.
• Heterogeneous training data. The model is trained from a mixture of web driving videos, real driving datasets with action or trajectory labels, and simulated data containing non-expert behavior.
• High-fidelity open-world prediction. ReSim targets realistic future driving video generation while improving controllability for both expert and non-expert actions.

News

• [2026/05/05] Initial public code release.

Release Roadmap

• [x] Initial code release with training and inference scripts.
• [x] Release pretrained ReSim world-model weights - pretrained on OpenDV + NavSim (expert actions only, without Carla data).
• [ ] Release pretrained ReSim world-model weights - pretrained on OpenDV + NavSim + CARLA data (expert + non-expert actions).

Getting Started

• Installation
• Checkpoint Preparation
• Data Preparation
• Training
• Inference
• Trouble Shooting

Installation

The main ReSim pipeline was developed with Python 3.10, PyTorch 2.4, CUDA 12.4, SAT, and DeepSpeed-style distributed training.

conda create -n resim python=3.10 -y

conda activate resim

pip install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt


cd SwissArmyTransformer
pip install -e . --no-build-isolation
cd ..

The launch scripts under sat/ also add the vendored SwissArmyTransformer directory to PYTHONPATH, so inference can run from a clean checkout after the Python dependencies are installed.

Checkpoint Preparation

ReSim uses SAT-format CogVideoX video diffusion checkpoints plus the text encoder and VAE components required by the configs. Download the public asset bundle from Hugging Face:

pip install -U huggingface_hub
huggingface-cli download OpenDriveLab-org/ReSim_Assets \
  --repo-type model \
  --local-dir checkpoints/CogVideoX-2b-sat

The deprecated Tsinghua mirror download for vae.zip and transformer.zip is no longer required. After downloading, the component directory should contain:

checkpoints/CogVideoX-2b-sat/
|-- transformer/
|   |-- latest
|   `-- <iteration>/mp_rank_00_model_states.pt
|-- vae/3d-vae.pt
`-- t5-v1_1-xxl/
    |-- config.json
    |-- model-00001-of-00002.safetensors
    |-- model-00002-of-00002.safetensors
    |-- model.safetensors.index.json
    |-- spiece.model
    `-- tokenizer_config.json

The transformer directory stores the SAT checkpoint, vae/3d-vae.pt is used by the video autoencoder, and t5-v1_1-xxl provides the frozen text encoder and tokenizer files. If you place the assets elsewhere, keep the same internal directory structure and update the config paths accordingly.

Before running training or inference, copy an example config and update:

• args.load: ReSim or base transformer checkpoint directory.
• model.conditioner_config...FrozenT5Embedder.params.model_dir: T5 directory.
• model.first_stage_config.params.ckpt_path: VAE checkpoint path.
• args.train_data and args.valid_data: dataset annotation files.

For example, the checkpoint-related fields should point to the downloaded asset directory:

args:
  load: "checkpoints/CogVideoX-2b-sat/transformer"

model:
  conditioner_config:
    params:
      emb_models:
        - params:
            model_dir: "checkpoints/CogVideoX-2b-sat/t5-v1_1-xxl"
  first_stage_config:
    params:
      ckpt_path: "checkpoints/CogVideoX-2b-sat/vae/3d-vae.pt"

Data Preparation

The ReSim loaders are JSON-driven. Real driving and simulator datasets use the shared schema consumed by sat/data_share.py:

{
  "meta": {
    "data_root": "/path/to/image/root"
  },
  "clips": [
    {
      "img_seq": ["scene/frame_000.jpg", "scene/frame_001.jpg"],
      "cmd": "Moving_Forward",
      "traj_fut": [[0.0, 0.0, 0.0], [1.0, 0.1, 0.0]],
      "lidar_pc_token": "sample-token"
    }
  ]
}

Important fields:

• img_seq is a list of frame paths relative to meta.data_root. The loader also supports img_seq_his plus img_seq_fut.
• cmd can be a string such as Moving_Forward, Turning_Left, or Turning_Right, or an integer mapped by sat/data_utils.py.
• traj_fut stores future trajectory points as [x, y, heading]. The default configs use 8 future points.
• lidar_pc_token or token is used to name generated outputs.

For web-driving data, sat/data_youtube.py expects clips with folder_name, first_frame, end_frame, and flow_direction.

Training

Run ReSim training through sat/train_video.py and the provided launcher:

cd sat

# CFG, GPUS, NNODES, optional SEED
bash finetune_multi_gpus_custom.sh configs/train.yaml 8 1 42

For single-GPU debugging:

cd sat
bash finetune_single_gpu_custom.sh configs/train.yaml

Before launching a real run, check the copied config:

• args.mode: finetune
• data.target, for example data_multi.MultiSourceDataset or data_waymo.WaymoDataset
• data.params.video_size, fps, max_num_frames, and crop mode
• DeepSpeed batch size, gradient accumulation, precision, and save interval
• train_data_weights when mixing heterogeneous data sources

Training writes checkpoints under args.save and stores the merged training config with the run.

Inference

Run ReSim sampling through sat/sample_video.py and the provided launcher:

cd sat
bash inference_custom.sh configs/infer_nus.yaml

The example inference config uses input_type: dataset; it loads validation clips, conditions on the first frames, optionally applies fut_traj, and writes MP4 samples.

Common inference options are config-driven:

• args.sampling_video_size: output frame size, for example [512, 896].
• args.sampling_num_frames: latent-frame count, commonly 13, 11, or 9.
• args.n_prediction_round: autoregressive rollout rounds.
• args.apply_traj: whether to condition on fut_traj.
• args.save_gt and args.concat_gt_for_demo: whether to save ground-truth clips and side-by-side demo videos.

Trouble Shooting

• ModuleNotFoundError: No module named 'sat': install the vendored SAT package with pip install -e SwissArmyTransformer.

• Paths in example configs must be replaced with paths on your machine before running.

Acknowledgement

This implementation builds on the SAT training stack from CogVideoX, SwissArmyTransformer, and other open-source video diffusion components. We thank all maintainers for their open-source contributions.

Citation

If this project is useful for your research, please cite:

@inproceedings{yang2025resim,
  title={ReSim: Reliable World Simulation for Autonomous Driving},
  author={Jiazhi Yang and Kashyap Chitta and Shenyuan Gao and Long Chen and Yuqian Shao and Xiaosong Jia and Hongyang Li and Andreas Geiger and Xiangyu Yue and Li Chen},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025}
}

License

The repository includes an Apache-2.0 LICENSE file. Model weights may be governed by separate terms in MODEL_LICENSE. Check the licenses of SAT, CARLA, nuScenes, Waymo, nuPlan, OpenDV, and any redistributed annotations before public release or commercial use.