MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control

• URL: http://arxiv.org/abs/2409.06189v2
• Date: Wed, 11 Sep 2024 11:50:27 GMT
• Title: MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control
• Authors: Yining Yao, Xi Guo, Chenjing Ding, Wei Wu,
• Abstract summary: MyGo is an end-to-end framework for driving video generation. MyGo introduces motion of onboard cameras as conditions to make progress in camera controllability and multi-view consistency. Results show that MyGo has achieved state-of-the-art results in both general camera-controlled video generation and multi-view driving video generation tasks.
• Score: 4.556249147612401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality driving video generation is crucial for providing training data for autonomous driving models. However, current generative models rarely focus on enhancing camera motion control under multi-view tasks, which is essential for driving video generation. Therefore, we propose MyGo, an end-to-end framework for video generation, introducing motion of onboard cameras as conditions to make progress in camera controllability and multi-view consistency. MyGo employs additional plug-in modules to inject camera parameters into the pre-trained video diffusion model, which retains the extensive knowledge of the pre-trained model as much as possible. Furthermore, we use epipolar constraints and neighbor view information during the generation process of each view to enhance spatial-temporal consistency. Experimental results show that MyGo has achieved state-of-the-art results in both general camera-controlled video generation and multi-view driving video generation tasks, which lays the foundation for more accurate environment simulation in autonomous driving. Project page: https://metadrivescape.github.io/papers_project/MyGo/page.html

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