Zero-shot Novel View Synthesis via Adaptive Modulating Video Diffusion Process
Keywords: Diffusion sampling, Novel view synthesis
TL;DR: Our method adaptively modulates score functions of a pre-trained video diffusion to synthesize impressive novel views.
Abstract: By harnessing the potent generative capabilities of pre-trained large video diffusion models, we propose a new novel view synthesis paradigm that operates \textit{without} the need for training. The proposed method adaptively modulates the diffusion sampling process with the given views to enable the creation of visually pleasing results from single or multiple views of static scenes or monocular videos of dynamic scenes. Specifically, built upon our theoretical modeling, we iteratively modulate the score function with the given scene priors represented with warped input views to control the video diffusion process. Moreover, by theoretically exploring the boundary of the estimation error, we achieve the modulation in an adaptive fashion according to the view pose and the number of diffusion steps. Extensive evaluations on both static and dynamic scenes substantiate the significant superiority of our method over state-of-the-art methods both quantitatively and qualitatively. The source code can be found on the anonymous webpage: https://github.com/PAPERID5494/VD_NVS. We also refer reviewers to the Supplementary Material for the video demo.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 5494
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