  By harnessing the potent generative capabilities of pre-trained large video
diffusion models, we propose NVS-Solver, a new novel view synthesis (NVS)
paradigm that operates \textit{without} the need for training. NVS-Solver
adaptively modulates the diffusion sampling process with the given views to
enable the creation of remarkable visual experiences 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 NVS-Solver over state-of-the-art methods both quantitatively
and qualitatively. \textit{ Source code in }
\href{https://github.com/ZHU-Zhiyu/NVS_Solver}{https://github.com/ZHU-Zhiyu/NVS$\_$Solver}.
