Novel View Extrapolation with Video Diffusion Priors
Authors: name: Kunhao Liu, name: Ling Shao, name: Shijian Lu

The field of novel view synthesis has made significant strides thanks to the
development of radiance field methods. However, most radiance field techniques
are far better at novel view interpolation than novel view extrapolation where
the synthesis novel views are far beyond the observed training views. We design
ViewExtrapolator, a novel view synthesis approach that leverages the generative
priors of Stable Video Diffusion (SVD) for realistic novel view extrapolation.
By redesigning the SVD denoising process, ViewExtrapolator refines the
artifact-prone views rendered by radiance fields, greatly enhancing the clarity
and realism of the synthesized novel views. ViewExtrapolator is a generic novel
view extrapolator that can work with different types of 3D rendering such as
views rendered from point clouds when only a single view or monocular video is
available. Additionally, ViewExtrapolator requires no fine-tuning of SVD,
making it both data-efficient and computation-efficient. Extensive experiments
demonstrate the superiority of ViewExtrapolator in novel view extrapolation.
Project page: https://kunhao-liu.github.io/ViewExtrapolator/.