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Panacea: Panoramic and Controllable Video Generation for Autonomous Driving

Official Repository of Panacea.

[Paper] Panacea: Panoramic and Controllable Video Generation for Autonomous Driving,
Yuqing Wen<sup>1*†</sup>, Yucheng Zhao<sup>2*</sup>,Yingfei Liu<sup>2*</sup>, Fan Jia<sup>2</sup>, Yanhui Wang<sup>1</sup>, Chong Luo<sup>1</sup>, Chi Zhang<sup>3</sup>, Tiancai Wang<sup>2‡</sup>, Xiaoyan Sun<sup>1‡</sup>, Xiangyu Zhang<sup>2</sup> <br> <sup>1</sup>University of Science and Technology of China, <sup>2</sup>MEGVII Technology, <sup>3</sup>Mach Drive <br> <sup>*</sup>Equal Contribution, <sup>†</sup>This work was done during the internship at MEGVII, <sup>‡</sup>Corresponding Author.

[Paper] Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving,
Yuqing Wen<sup>1*†</sup>, Yucheng Zhao<sup>2*</sup>,Yingfei Liu<sup>2*</sup>, Binyuan Huang<sup>4*</sup>, Fan Jia<sup>2</sup>, Yanhui Wang<sup>1</sup>, Chi Zhang<sup>3</sup>, Tiancai Wang<sup>2‡</sup>, Xiaoyan Sun<sup>1‡</sup>, Xiangyu Zhang<sup>2</sup> <br> <sup>1</sup>University of Science and Technology of China, <sup>2</sup>MEGVII Technology, <sup>3</sup>Mach Drive, <sup>4</sup>Wuhan University <br> <sup>*</sup>Equal Contribution, <sup>†</sup>This work was done during the internship at MEGVII, <sup>‡</sup>Corresponding Author.

[WebPage] https://panacea-ad.github.io/

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Getting Started

Please follow our documentation step by step.

Environment Setup

Following the instruction from: Environment Setup.

Prepare dataset

Prepare real dataset following the instruction from Data Preparation.

Remember to put the dataset under the path data/nuscenes

Download pretrained checkpoint

Download the weights of the second stage from panaceaplus_40k_deepspeed.ckpt

Put it to folder checkpoints/

Inference

--split: to specify train or val sets

--use_last_frame=true means use the last frame as conditional image.

Run the following command to inference stage 2 on the whole training/val set of nuscenes.

python -m torch.distributed.launch --nproc_per_node=8 --master_port=1238 inference.py --base configs/inference_nuscenes.yaml --ckptpath --ckpt checkpoints/panaceaplus_40k_deepspeed.ckpt --split train --use_last_frame true --name EXP_NAME --bs 1
<div class="root-content" style="padding-top: 10px; width: 65%;"> <h1 class="section-name">Generating <font style="color: red;">Multi-View and Controllable</font> Videos for Autonoumous Driving</h1> <img src="assests/pipeline.png" style="margin:auto; right: 0; left: 0; width: 90%; display: inline;"> <p class="section-content-text" style="padding-bottom: 20px;"><strong>Overview of Panacea. </strong>(a). The diffusion training process of Panacea, enabled by a diffusion encoder and decoder with the decomposed 4D attention module. (b). The decomposed 4D attention module comprises three components: intra-view attention for spatial processing within individual views, cross-view attention to engage with adjacent views, and cross-frame attention for temporal processing. (c). Controllable module for the integration of diverse signals. The image conditions are derived from a frozen VAE encoder and combined with diffused noises. The text prompts are processed through a frozen CLIP encoder, while BEV sequences are handled via ControlNet. (d). The details of BEV layout sequences, including projected bounding boxes, object depths, road maps and camera pose.</p> <img src="assests/pipeline_inference.png" style="margin:auto; right: 0; left: 0; width: 65%; display: inline;"> <p class="section-content-text" style="padding-bottom: 20px;"><strong>The two-stage inference pipeline of Panacea.</strong> Its two-stage process begins by creating multi-view images with BEV layouts, followed by using these images, along with subsequent BEV layouts, to facilitate the generation of following frames.</p> </div> <div class="root-content" style="padding-top: 10px; width: 65%; padding-bottom: 10px;"> <h1 class="section-name">&#127916;&nbsp;&nbsp; BEV-guided Video Generation &nbsp;&nbsp;&#127916;</h1> <table style="width: 100%;"> <tbody> <tr class="result-row"> <td> <img src="assests/demo1.gif"> </td> </tr> <tr class="result-row"> <td> <img src="assests/demo2.gif"> </td> </tr> </tbody> </table> <p class="section-content-text"><strong>Controllable multi-view video generation. Panacea is able to generate realistic, controllable videos with good temporal and view consistensy.</strong></p> </div> <div class="root-content" style="padding-top: 10px;width: 65%; padding-bottom: 10px;"> <h1 class="section-name">&#127902;&nbsp;&nbsp; Attribute Controllable Video Generation &nbsp;&nbsp;&#127902;</h1> <table style="width: 100%;"> <tbody> <tr class="result-row"> <td> <img src="assests/attribute.png"> </td> </tr> </tbody> </table> <p class="section-content-text"><strong>Video generation with variable attribute controls, such as weather, time, and scene, which allows Panacea to simulate a variety of rare driving scenarios, including extreme weather conditions such as rain and snow, thereby greatly enhancing the diversity of the data.</strong></p> </div> <div class="root-content" style="padding-top: 10px;width: 65%; padding-bottom: 10px;"> <h1 class="section-name">&#128293;&nbsp;&nbsp; Benefiting Autonomous Driving &nbsp;&nbsp;&#128293;</h1> <table style="padding-left: 120px;width: 90%;"> <tbody> <tr class="result-row"> <td> <img src="assests/gain.png"> </td> </tr> </tbody> </table> <p class="section-content-text"><strong> (a). Panoramic video generation based on BEV (Bird’s-Eye-View) layout sequence facilitates the establishment of a synthetic video dataset, which enhances perceptual tasks. (b). Producing panoramic videos with conditional images and BEV layouts can effectively elevate image-only datasets to video datasets, thus enabling the advancement of video-based perception techniques.</strong></p> </div> <div style="background-color: white; margin-right: auto; margin-left: auto;"> <div class="root-content" style="padding-top: 10px; width: 65%; padding-bottom: 10px;"> <div> <h1 class="section-name" style="margin-top: 30px; text-align: left; font-size: 25px;"> BibTex </h1> <a name="bib"></a> <pre style="margin-top: 5px;" class="bibtex"> <code> @inproceedings{wen2024panacea, title={Panacea: Panoramic and controllable video generation for autonomous driving}, author={Wen, Yuqing and Zhao, Yucheng and Liu, Yingfei and Jia, Fan and Wang, Yanhui and Luo, Chong and Zhang, Chi and Wang, Tiancai and Sun, Xiaoyan and Zhang, Xiangyu}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6902--6912}, year={2024} } @misc{wen2024panaceapanoramiccontrollablevideo, title={Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving}, author={Yuqing Wen and Yucheng Zhao and Yingfei Liu and Binyuan Huang and Fan Jia and Yanhui Wang and Chi Zhang and Tiancai Wang and Xiaoyan Sun and Xiangyu Zhang}, year={2024}, eprint={2408.07605}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2408.07605}, } }</code></pre> </div> <div style="margin-bottom: 0px;"> <h1 class="section-name" style="margin-top: 0px; margin-bottom: 10px; text-align: left; font-size: 25px;"> Contact </h1> <p class="section-content-text"> Feel free to contact us at <strong>wenyuqing AT mail.ustc.edu.cn</strong> or <strong>wangtiancai AT megvii.com</strong> </div> </div> </div>

Acknowledgement

This code builds on Stability-AI, ControlNet and StreamPETR. Thanks for open-sourcing!