Abstract
Recent advances in AI-generated video have shown strong performance on \emph{text-to-video} tasks, particularly for short clips depicting a single scene. However, current models struggle to generate longer videos with coherent scene transitions, primarily because they cannot infer when a transition is needed from the prompt. Most open-source models are trained on datasets consisting of single-scene video clips, which limits their capacity to learn and respond to prompts requiring multiple scenes. Developing scene transition awareness is essential for multi-scene generation, as it allows models to identify and segment videos into distinct clips by accurately detecting transitions. To address this, we propose the \textbf{Transition-Aware Video} (TAV) dataset, which consists of preprocessed video clips with multiple scene transitions. Our experiment shows that post-training on the \textbf{TAV} dataset improves prompt-based scene transition understanding, narrows the gap between required and generated scenes, and maintains image quality.
Abstract (translated)
最近在AI生成视频领域的进展显示，在将文本转换为短视频的任务中表现出色，尤其是对于描绘单一场景的短片段。然而，当前的模型难以生成包含连贯场景过渡的较长视频，因为它们无法从提示中推断出何时需要进行场景切换。大多数开源模型都是基于由单个场景组成的视频剪辑的数据集训练出来的，这限制了它们对要求多场景响应的提示的学习和应对能力。为了提高多场景生成的效果，发展场景转换意识至关重要，它使模型能够准确地检测并分割视频中的不同片段。为了解决这个问题，我们提出了**过渡感知视频（Transition-Aware Video, TAV）数据集**，该数据集包含具有多个场景转换的预处理视频剪辑。我们的实验表明，在TAV数据集上进行后期训练可以改善基于提示的场景转换理解能力，缩小所需场景与生成场景之间的差距，并保持图像质量。
URL
https://arxiv.org/abs/2507.18046
