TVG: A Training-free Transition Video Generation Method with Diffusion
  Models
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.
66cd3aca1fa7548061f2bf86
Rui Zhang
66cd3aca1fa7548061f2bf87
Yaosen Chen
admin_assigned
2024-08-27T13:27:51.319Z
64b62160fafb5319dc91ce91
/avatars/cfe76d802c776a4913397f9a86d1bf36.svg
Chen Yaosen
user
Chenyaosen
66cd3aca1fa7548061f2bf88
Yuegen Liu
admin_assigned
2024-08-27T13:28:02.737Z
64ba4b4a5e08ebed3e16a7d4
/avatars/6e4094dcd10e5cd2b51ca10300edc7fb.svg
liuyuegen
user
tqbmxt
66cd3aca1fa7548061f2bf89
Wei Wang
66cd3aca1fa7548061f2bf8a
Xuming Wen
66cd3aca1fa7548061f2bf8b
Hongxia Wang
admin_assigned
2024-08-27T13:29:00.473Z
65df25acbaef64b80cc83fd1
/avatars/fbcc9cb013c08968a46a1a224eb23aac.svg
Wang Hongxiang
user
23whx
TVG: A Training-free Transition Video Generation Method with Diffusion
  Models
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.
A novel training-free method for generating smooth transition videos using video-level diffusion models, Gaussian Process Regression, and a Frequency-aware Bidirectional Fusion architecture.
diffusion model-based video generation Gaussian Process Regression (GPR) interpolation-based conditional controls Frequency-aware Bidirectional Fusion (FBiF) video-level diffusion models