Panacea: Panoramic and Controllable Video Generation for Autonomous Driving

Yuqing Wen^1*†, Yucheng Zhao^2*, Yingfei Liu^2*, Fan Jia², Yanhui Wang¹,
Chong Luo¹, Chi Zhang³, Tiancai Wang^2‡, Xiaoyan Sun^1‡, Xiangyu Zhang²

¹University of Science and Technology of China, ²MEGVII Technology, ³Mach Drive

^*Equal Contribution, ^†This work was done during the internship at MEGVII, ^‡Corresponding Author.

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Generating Multi-View and Controllable Videos for Autonoumous Driving

Overview of Panacea. (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.

The two-stage inference pipeline of Panacea. 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.

🎞 Attribute Controllable Video Generation 🎞

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.

🔥 Benefiting Autonomous Driving 🔥

(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.

BibTex

                
@artical{@misc{wen2023panacea,
    title={Panacea: Panoramic and Controllable Video Generation for Autonomous Driving}, 
    author={Yuqing Wen and Yucheng Zhao and Yingfei Liu and Fan Jia and Yanhui Wang and Chong Luo and Chi Zhang and Tiancai Wang and Xiaoyan Sun and Xiangyu Zhang},
    year={2023},
    eprint={2311.16813},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
}

Contact

Feel free to contact us at wenyuqing AT mail.ustc.edu.cn or wangtiancai AT megvii.com