VISTA: 基于扩散变换器的三元组监督视频风格迁移
VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers
AI总结：
本文提出VISTA方法，通过引入大规模三元组数据和基于扩散变换器的框架，解决视频风格迁移中风格、内容和运动的联合建模与解耦问题，实现了高质量的风格迁移效果。
摘要：
视频风格迁移旨在在保持内容、结构和运动的同时将视频渲染成目标艺术风格。尽管图像风格化技术已迅速发展，但视频风格化仍具有挑战性，因为存在时间不一致的问题。现有的大多数方法对帧或关键帧进行风格化，并通过启发式的时序传播来强制一致性，这在遮挡、遮挡解除和长期运动下容易产生漂移和闪烁伪影。我们提出VISTA-1000，一个包含1000种风格和运动对齐三元组的数据集（风格参考、干净视频、风格化视频），并提出一种基于扩散变换器的上下文视频风格迁移框架，具有轻量级的风格适配器以实现稳健的风格提取。大量实验表明，该方法在风格保真度、时间一致性和内容保持方面均达到最佳性能。
英文摘要：
Video style transfer aims to render videos in a target artistic style while preserving content, structure, and motion. While image stylization has advanced rapidly, video stylization remains challenging due to temporal inconsistency. Most existing methods stylize frames or keyframes and enforce consistency via heuristic temporal propagation, which is brittle under occlusions, disocclusions, and long-term motion, leading to drift and flickering artifacts. We argue that a fundamental bottleneck lies in the lack of large-scale triplet data and a principled training paradigm that jointly models and disentangles style, content, and motion.To address this, we introduce VISTA-1000, a synthetic dataset with 1,000 styles and motion-aligned triplets of style reference, clean video, and stylized video, and propose a diffusion-transformer-based in-context video style transfer framework with a lightweight style adapter for robust style extraction. Extensive experiments demonstrate SOTA performance in style fidelity, temporal consistency, and content preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
