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README.md

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----
-license: apache-2.0
----
-
-Data packages used by the OpenDWM project.
+---
+license: apache-2.0
+task_categories:
+- text-to-video
+- text-to-3d
+tags:
+- autonomous-driving
+- video-generation
+- 3d-reconstruction
+- diffusion-models
+- lidar
+---
+
+# OpenDWM Data Packages
+
+This repository contains data packages used by the [Open Driving World Models (OpenDWM)](https://github.com/SenseTime-FVG/OpenDWM) project. The OpenDWM initiative focuses on autonomous driving video generation, offering a high-quality, controllable tool for generating multi-view videos and 4D reconstructions.
+
+The data packages support research presented in papers such as [CVD-STORM: Cross-View Video Diffusion with Spatial-Temporal Reconstruction Model for Autonomous Driving](https://huggingface.co/papers/2510.07944). This work proposes CVD-STORM, a cross-view video diffusion model leveraging a spatial-temporal reconstruction Variational Autoencoder (VAE) to generate long-term, multi-view videos with 4D reconstruction capabilities under various control inputs. This is crucial for enabling generative models in world modeling for environment simulation and future state prediction in autonomous driving.
+
+**Paper**: [CVD-STORM: Cross-View Video Diffusion with Spatial-Temporal Reconstruction Model for Autonomous Driving](https://huggingface.co/papers/2510.07944)
+**Project Page**: [https://sensetime-fvg.github.io/CVD-STORM/](https://sensetime-fvg.github.io/CVD-STORM/)
+**Code**: [https://github.com/SenseTime-FVG/OpenDWM](https://github.com/SenseTime-FVG/OpenDWM)
+
+## Data Packages
+
+These data packages are essential resources for training and running examples within the OpenDWM project, particularly for layout-conditioned video and LiDAR generation tasks. Examples of such packages, referenced in the OpenDWM GitHub repository, include:
+
+*   [`nuscenes_scene-0627_package.zip`](https://huggingface.co/datasets/wzhgba/opendwm-data/resolve/main/nuscenes_scene-0627_package.zip?download=true): Layout resources based on the nuScenes dataset, used for video generation examples.
+*   [`carla_town04_package.zip`](https://huggingface.co/datasets/wzhgba/opendwm-data/resolve/main/carla_town04_package.zip?download=true): Layout resources based on the Carla simulation environment, also used for video generation examples.
+*   [`nuscenes_scene-0627_lidar_package.zip`](https://huggingface.co/datasets/wzhgba/opendwm-data/resolve/main/nuscenes_scene-0627_lidar_package.zip?download=true): LiDAR data package for LiDAR generation examples.
+
+## Sample Usage
+
+This section provides instructions to set up the OpenDWM environment and run examples for generating driving videos and LiDAR data using the models and code from the [OpenDWM GitHub repository](https://github.com/SenseTime-FVG/OpenDWM).
+
+### 1. Setup
+
+First, ensure you have PyTorch (>= 2.5) installed. Then, clone the OpenDWM repository and install its dependencies:
+
+```bash
+python -m pip install torch==2.5.1 torchvision==0.20.1 # Or a compatible PyTorch version
+cd OpenDWM
+git submodule update --init --recursive
+python -m pip install -r requirements.txt
+```
+
+### 2. T2I, T2V generation with CTSD pipeline
+
+Download a base model (for VAE, text encoders, scheduler config) and a driving generation model checkpoint (e.g., from [wzhgba/opendwm-models](https://huggingface.co/wzhgba/opendwm-models)). Edit the [path](https://github.com/SenseTime-FVG/OpenDWM/blob/main/examples/ctsd_35_6views_image_generation.json#L102) and [prompts](https://github.com/SenseTime-FVG/OpenDWM/blob/main/examples/ctsd_35_6views_image_generation.json#L221) in your JSON config file (e.g., `examples/ctsd_35_6views_image_generation.json`), then run this command:
+
+```bash
+PYTHONPATH=src python examples/ctsd_generation_example.py -c examples/ctsd_35_6views_image_generation.json -o output/ctsd_35_6views_image_generation
+```
+
+### 3. Layout conditioned T2V generation with CTSD pipeline
+
+1.  Download a base model and driving generation model checkpoint, and edit the [path](https://github.com/SenseTime-FVG/OpenDWM/blob/main/examples/ctsd_35_6views_video_generation_with_layout.json#L156) in your JSON config file.
+2.  Download a layout resource package (e.g., [`nuscenes_scene-0627_package.zip`](https://huggingface.co/datasets/wzhgba/opendwm-data/resolve/main/nuscenes_scene-0627_package.zip?download=true) from this dataset, or [`carla_town04_package`](https://huggingface.co/datasets/wzhgba/opendwm-data/resolve/main/carla_town04_package.zip?download=true)) and unzip it to a specified `{RESOURCE_PATH}`. Then, edit the meta [path](https://github.com/SenseTime-FVG/OpenDWM/blob/main/examples/ctsd_35_6views_video_generation_with_layout.json#L162) in the JSON config as `{RESOURCE_PATH}/data.json`.
+3.  Run this command to generate the video:
+
+```bash
+PYTHONPATH=src python src/dwm/preview.py -c examples/ctsd_35_6views_video_generation_with_layout.json -o output/ctsd_35_6views_video_generation_with_layout
+```
+
+### 4. Layout conditioned LiDAR generation with Diffusion pipeline
+
+1.  Download LiDAR VAE and LiDAR Diffusion generation model checkpoints (e.g., from [wzhgba/opendwm-models](https://huggingface.co/wzhgba/opendwm-models)).
+2.  Prepare the dataset (e.g., [`nuscenes_scene-0627_lidar_package.zip`](https://huggingface.co/datasets/wzhgba/opendwm-data/resolve/main/nuscenes_scene-0627_lidar_package.zip?download=true) from this dataset).
+3.  Modify the values of `json_file`, `autoencoder_ckpt_path`, and `diffusion_model_ckpt_path` to the paths of your dataset and checkpoints in the json file `examples/lidar_diffusion_temporal_preview.json`.
+4.  Run the following command to generate LiDAR data according to the reference frame autoregressively:
+
+```bash
+PYTHONPATH=src python3 -m torch.distributed.run --nnodes 1 --nproc-per-node 2 --node-rank 0 --master-addr 127.0.0.1 --master-port 29000 src/dwm/preview.py -c examples/lidar_diffusion_temporal_preview.json -o output/temporal_diffusion
+```
+
+## Citation
+
+If you find the OpenDWM project useful in your research or refer to the provided baseline results, please star :star: its repository and consider citing:
+
+```bibtex
+@misc{opendwm,
+  Year = {2025},
+  Note = {https://github.com/SenseTime-FVG/OpenDWM},
+  Title = {OpenDWM: Open Driving World Models}
+}
+```