标题： 基于图像到视频扩散的多功能过渡生成 显示英文标题

标题： Versatile Transition Generation with Image-to-Video Diffusion

摘要： 利用文本、图像、结构图或运动轨迹作为条件引导，扩散模型在自动且高质量的视频生成方面取得了巨大成功。 然而，在给定第一帧和最后一帧视频以及描述性文本提示的情况下生成平滑且合理的过渡视频仍研究不足。 我们提出了VTG，一个多功能的过渡视频生成框架，能够生成平滑、高保真且语义连贯的视频过渡。 VTG引入了基于插值的初始化，有助于保持对象身份并有效处理突然的内容变化。 此外，它结合了双向运动微调和表示对齐正则化，分别缓解预训练图像到视频扩散模型在运动平滑性和生成保真度方面的局限性。 为了评估VTG并促进未来在统一过渡生成方面的研究，我们收集了TransitBench，一个涵盖两个代表性过渡任务的全面基准：概念融合和场景过渡。 大量实验表明，VTG在所有四个任务中始终表现出优越的过渡性能。 显示英文摘要

摘要： 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.