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arXiv · 2508.01698

Versatile Transition Generation with Image-to-Video Diffusion

Zuhao Yang·Jiahui Zhang·Yingchen Yu·Shijian Lu·Song Bai
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Abstract

Leveraging text, images, structure maps, or motion trajectories as conditional guidance, diffusion models have achieved great success in automated and high-quality video generation. However, generating smooth and rational transition videos given the first and last video frames as well as descriptive text prompts is far underexplored. We present VTG, a Versatile Transition video Generation framework that can generate smooth, high-fidelity, and semantically coherent video transitions. VTG introduces interpolation-based initialization that helps preserve object identity and handle abrupt content changes effectively. In addition, it incorporates dual-directional motion fine-tuning and representation alignment regularization to mitigate the limitations of pre-trained image-to-video diffusion models in motion smoothness and generation fidelity, respectively. To evaluate VTG and facilitate future studies on unified transition generation, we collected TransitBench, a comprehensive benchmark for transition generation covering two representative transition tasks: concept blending and scene transition. Extensive experiments show that VTG achieves superior transition performance consistently across all four tasks.

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Zuhao Yang, Jiahui Zhang, Yingchen Yu, Shijian Lu, Song Bai. 2025-08-03. Versatile Transition Generation with Image-to-Video Diffusion. https://arxiv.org/abs/2508.01698

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