title: TVG: A Training-free Transition Video Generation Method with Diffusion
    Models
summary: Transition videos play a crucial role in media production, enhancing the flow
  and coherence of visual narratives. Traditional methods like morphing often
  lack artistic appeal and require specialized skills, limiting their
  effectiveness. Recent advances in diffusion model-based video generation offer
  new possibilities for creating transitions but face challenges such as poor
  inter-frame relationship modeling and abrupt content changes. We propose a
  novel training-free Transition Video Generation (TVG) approach using
  video-level diffusion models that addresses these limitations without
  additional training. Our method leverages Gaussian Process Regression
  (GPR) to model latent representations, ensuring smooth and dynamic
  transitions between frames. Additionally, we introduce interpolation-based
  conditional controls and a Frequency-aware Bidirectional Fusion (FBiF)
  architecture to enhance temporal control and transition reliability.
  Evaluations of benchmark datasets and custom image pairs demonstrate the
  effectiveness of our approach in generating high-quality smooth transition
  videos. The code are provided in https://sobeymil.github.io/tvg.com.
paper_authors._id: 66cd3aca1fa7548061f2bf86
paper_authors.name: Rui Zhang
paper_authors._id: 66cd3aca1fa7548061f2bf87
paper_authors.name: Yaosen Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-27T13:27:51.319Z
paper_authors._id: 64b62160fafb5319dc91ce91
paper_authors.avatarUrl: /avatars/cfe76d802c776a4913397f9a86d1bf36.svg
paper_authors.fullname: Chen Yaosen
paper_authors.type: user
paper_authors.user: Chenyaosen
paper_authors._id: 66cd3aca1fa7548061f2bf88
paper_authors.name: Yuegen Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-27T13:28:02.737Z
paper_authors._id: 64ba4b4a5e08ebed3e16a7d4
paper_authors.avatarUrl: /avatars/6e4094dcd10e5cd2b51ca10300edc7fb.svg
paper_authors.fullname: liuyuegen
paper_authors.type: user
paper_authors.user: tqbmxt
paper_authors._id: 66cd3aca1fa7548061f2bf89
paper_authors.name: Wei Wang
paper_authors._id: 66cd3aca1fa7548061f2bf8a
paper_authors.name: Xuming Wen
paper_authors._id: 66cd3aca1fa7548061f2bf8b
paper_authors.name: Hongxia Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-27T13:29:00.473Z
paper_authors._id: 65df25acbaef64b80cc83fd1
paper_authors.avatarUrl: /avatars/fbcc9cb013c08968a46a1a224eb23aac.svg
paper_authors.fullname: Wang Hongxiang
paper_authors.type: user
paper_authors.user: 23whx
paper_title: TVG: A Training-free Transition Video Generation Method with Diffusion
    Models
paper_summary: Transition videos play a crucial role in media production, enhancing the flow
  and coherence of visual narratives. Traditional methods like morphing often
  lack artistic appeal and require specialized skills, limiting their
  effectiveness. Recent advances in diffusion model-based video generation offer
  new possibilities for creating transitions but face challenges such as poor
  inter-frame relationship modeling and abrupt content changes. We propose a
  novel training-free Transition Video Generation (TVG) approach using
  video-level diffusion models that addresses these limitations without
  additional training. Our method leverages Gaussian Process Regression
  (GPR) to model latent representations, ensuring smooth and dynamic
  transitions between frames. Additionally, we introduce interpolation-based
  conditional controls and a Frequency-aware Bidirectional Fusion (FBiF)
  architecture to enhance temporal control and transition reliability.
  Evaluations of benchmark datasets and custom image pairs demonstrate the
  effectiveness of our approach in generating high-quality smooth transition
  videos. The code are provided in https://sobeymil.github.io/tvg.com.
paper_ai_keywords: diffusion model video-level diffusion models Gaussian Process Regression ($\mathcal{GPR}$) latent representations interpolation-based conditional controls Frequency-aware Bidirectional Fusion (FBiF)