Project page | Paper | Data | Video
Jing Tan*, Shuai Yang*, Tong Wu✉️, Jingwen He, Yuwei Guo, Ziwei Liu, Dahua Lin✉️
* Equal Contribution,✉️ Corresponding author
✨ Updates
[2025-03-19] 🔥 Release our curated 360 video dataset YouTube360.
[2025-01-30] Release inference code and checkpoints!
📷 Quick Demos(only show panoramic video here)
More results can be found on our Project Gallery.
📖 VR Mode Guideline
We highly recommend using a mobile phone to access the website(better use Chrome browser) for device motion tracking, enhancing the immersive quality of the VR interactive experience.
🔥The Loading may be a little slow, but your wait will be worth it !!!
Dataset
Please refer to dataset/dataset.md for the metadata of our curated YouTube videos.
🔧 Steps for Inference
Prepare Environment
git clone https://github.com/3DTopia/Imagine360.git
cd Imagine360
conda create -n imagine360 python==3.10
conda activate imagine360
pip install -r requirements.txt
- Use GeoCalibration as elevation estimation model (by default):
python -m pip install -e "git+https://github.com/cvg/GeoCalib#egg=geocalib"
- Use PerspectiveFields as elevation estimation model:
pip install git+https://github.com/jinlinyi/PerspectiveFields.git
Download Weights
Download our checkpoints from google drive, and also [sam_vit_b_01ec64], [stable-diffusion-2-1], and [Qwen-VL-Chat].
Update the paths to these pre-trained models in configs/prompt-dual.yaml.
Perspective-to-360 Video Generation
python inference_dual_p2e.py --config configs/prompt-dual.yaml
If the result does not align with expectations, try modify text prompt or set different seeds (-1 for random seed) in configs/prompt-dual.yaml.
Super Resolution [Optional]
For better visualization under VR mode, we recommend to use VEnhancer for video super resolution. Follow the instructions to update VEnhancer code for 360 close-loop continuity.
📧 Contact Us
Jing Tan: tj023@ie.cuhk.edu.hk
Shuai Yang: yang_shuai@sjtu.edu.cn
Tong Wu: wutong16@stanford.edu
📆 Todo
- Release Inference Code
- Release Dataset
- Gradio Demo
- Release Train Code
📚 Acknowledgements
Special thanks to PanFusion, FollowYourCanvas, 360DVD and AnimateDiff for codebase and pre-trained weights.
✒️ Citation
If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝
@article{tan2024imagine360, title={Imagine360: Immersive 360 Video Generation from Perspective Anchor}, author={Tan, Jing and Yang, Shuai and Wu, Tong and He, Jingwen and Guo, Yuwei and Liu, Ziwei and Lin, Dahua}, journal={arXiv preprint arXiv:2412.03552}, year={2024} }
