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 ($\mathcal{GPR}$) 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.

本研究解决了传统过渡视频生成方法在艺术性和技能要求上的不足，提出了一种新颖的无训练过渡视频生成（TVG）方法。通过使用视频级扩散模型及高斯过程回归来建模潜在表示，确保帧间平滑动态过渡，评估结果显示该方法在生成高质量过渡视频方面具有显著效果。

TVG：一种无训练的过渡视频生成方法与扩散模型