title: Novel View Extrapolation with Video Diffusion Priors
summary: 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/.
paper_authors._id: 6740bb4ad3ad4510c18306ed
paper_authors.name: Kunhao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-24T22:06:52.908Z
paper_authors._id: 660b79aa822b119f215c3488
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/660b79aa822b119f215c3488/le_EuzAz6mhW0FQ9J0knV.jpeg
paper_authors.fullname: Kunhao Liu
paper_authors.type: user
paper_authors.user: KunhaoLiu
paper_authors._id: 6740bb4ad3ad4510c18306ee
paper_authors.name: Ling Shao
paper_authors._id: 6740bb4ad3ad4510c18306ef
paper_authors.name: Shijian Lu
paper_title: Novel View Extrapolation with Video Diffusion Priors
paper_summary: 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/.
paper_ai_keywords: radiance field novel view synthesis novel view interpolation novel view extrapolation ViewExtrapolator generative priors Stable Video Diffusion (SVD) denoising process artifact-prone views clarity realism 3D rendering point clouds monocular video data-efficient computation-efficient