Abstract
We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in a target style specified by a text prompt. A major challenge is the lack of paired video data for supervision. We propose PickStyle, a video-to-video style transfer framework that augments pretrained video diffusion backbones with style adapters and benefits from paired still image data with source-style correspondences for training. PickStyle inserts low-rank adapters into the self-attention layers of conditioning modules, enabling efficient specialization for motion-style transfer while maintaining strong alignment between video content and style. To bridge the gap between static image supervision and dynamic video, we construct synthetic training clips from paired images by applying shared augmentations that simulate camera motion, ensuring temporal priors are preserved. In addition, we introduce Context-Style Classifier-Free Guidance (CS-CFG), a novel factorization of classifier-free guidance into independent text (style) and video (context) directions. CS-CFG ensures that context is preserved in generated video while the style is effectively transferred. Experiments across benchmarks show that our approach achieves temporally coherent, style-faithful, and content-preserving video translations, outperforming existing baselines both qualitatively and quantitatively.
Abstract (translated)
我们研究了使用扩散模型进行视频风格迁移的任务，目标是在保持输入视频上下文的同时将其渲染为由文本提示指定的目标风格。一个主要的挑战是缺乏配对视频数据以供监督训练。为此，我们提出了PickStyle框架，这是一种视频到视频的风格迁移框架，它在预先训练好的视频扩散主干网络中加入了风格适配器，并利用带有源风格对应关系的成对静止图像数据进行训练。PickStyle通过在条件模块的自我注意层中插入低秩适配器来实现高效的运动-风格传输专业化，同时保持视频内容和风格之间的一致性。 为了弥合静态图像监督与动态视频之间的差距，我们通过施加模拟摄像机移动的共享增强处理，从成对静止图象构建合成训练片段，以确保时间先验得以保留。此外，我们引入了上下文-样式无分类器引导（Context-Style Classifier-Free Guidance, CS-CFG），这是一种新颖的将无分类器引导分解为独立的文字（风格）和视频（上下文）方向的方法。CS-CFG确保在生成的视频中保持内容一致性的同时有效迁移风格。 跨基准实验表明，我们的方法实现了时间上连贯、风格忠实且内容保留的视频转换，在定性和定量评估中均优于现有基线方法。
URL
https://arxiv.org/abs/2510.07546
