360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model

Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, Jian Zhang

Published 2024-01-12Version 1

360-degree panoramic videos recently attract more interest in both studies and applications, courtesy of the heightened immersive experiences they engender. Due to the expensive cost of capturing 360-degree panoramic videos, generating desirable panoramic videos by given prompts is urgently required. Recently, the emerging text-to-video (T2V) diffusion methods demonstrate notable effectiveness in standard video generation. However, due to the significant gap in content and motion patterns between panoramic and standard videos, these methods encounter challenges in yielding satisfactory 360-degree panoramic videos. In this paper, we propose a controllable panorama video generation pipeline named 360-Degree Video Diffusion model (360DVD) for generating panoramic videos based on the given prompts and motion conditions. Concretely, we introduce a lightweight module dubbed 360-Adapter and assisted 360 Enhancement Techniques to transform pre-trained T2V models for 360-degree video generation. We further propose a new panorama dataset named WEB360 consisting of 360-degree video-text pairs for training 360DVD, addressing the absence of captioned panoramic video datasets. Extensive experiments demonstrate the superiority and effectiveness of 360DVD for panorama video generation. The code and dataset will be released soon.