DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes

• URL: http://arxiv.org/abs/2409.04003v2
• Date: Mon, 25 Nov 2024 03:50:30 GMT
• Title: DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenes
• Authors: Jianbiao Mei, Xuemeng Yang, Licheng Wen, Tao Hu, Yu Yang, Tiantian Wei, Yukai Ma, Min Dou, Botian Shi, Yong Liu,
• Abstract summary: We propose DreamForge, an advanced diffusion-based autoregressive video generation model tailored for 3D-controllable long-term generation. To enhance the lane and foreground generation, we introduce perspective guidance and integrate object-wise position encoding. We also propose motion-aware temporal attention to capture motion cues and appearance changes in videos.
• Score: 15.506076058742744
• License:
• Abstract: Recent advances in diffusion models have improved controllable streetscape generation and supported downstream perception and planning tasks. However, challenges remain in accurately modeling driving scenes and generating long videos. To alleviate these issues, we propose DreamForge, an advanced diffusion-based autoregressive video generation model tailored for 3D-controllable long-term generation. To enhance the lane and foreground generation, we introduce perspective guidance and integrate object-wise position encoding to incorporate local 3D correlation and improve foreground object modeling. We also propose motion-aware temporal attention to capture motion cues and appearance changes in videos. By leveraging motion frames and an autoregressive generation paradigm, we can autoregressively generate long videos (over 200 frames) using a 7-frame model, achieving superior quality compared to the baseline in 16-frame video evaluations. Finally, we integrate our method with the realistic simulation platform DriveArena to provide more reliable open-loop and closed-loop evaluations for vision-based driving agents. The project page is available at https://pjlab-adg.github.io/DriveArena/dreamforge.

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