[Paper] [Project] [4D Dataset] [BibTeX]
vividzoo-video.mp4
While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new challenges posed by T2MVid generation lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution. To this end, we propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text. Specifically, we factor the T2MVid problem into viewpointspace and time components. Such factorization allows us to combine and reuse layers of advanced pre-trained multi-view image and 2D video diffusion models to ensure multi-view consistency as well as temporal coherence for the generated multi-view videos, largely reducing the training cost. We further introduce alignment modules to align the latent spaces of layers from the pre-trained multi-view and the 2D video diffusion models, addressing the reused layers’ incompatibility that arises from the domain gap between 2D and multi-view data. To facilitate this research line, we further contribute a captioned multi-view video dataset. Experimental results demonstrate that our method generates high-quality multi-view videos, exhibiting vivid motions, temporal coherence, and multi-view consistency, given a variety of text prompts.
[06/14/2024] We have released paper!
[06/17/2024] We have released our 4D Dataset!
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@misc{li2024vividzoo,
title={Vivid-ZOO: Multi-View Video Generation with Diffusion Model},
author={Bing Li and Cheng Zheng and Wenxuan Zhu and Jinjie Mai and Biao Zhang and Peter Wonka and Bernard Ghanem},
year={2024},
eprint={2406.08659},
archivePrefix={arXiv},
}
