Panacea: Panoramic and Controllable Video Generation for Autonomous Driving

Yuqing Wen1*†,    Yucheng Zhao2*,    Yingfei Liu2*,    Fan Jia2,    Yanhui Wang1,   
Chong Luo1,    Chi Zhang3,    Tiancai Wang2‡,    Xiaoyan Sun1‡,    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.

🎬   BEV-guided Video Generation   🎬

Controllable multi-view video generation. Panacea is able to generate realistic, controllable videos with good temporal and view consistensy.

🎞   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.


    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},


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