Abstract
Video style transfer aims to alter the style of a video while preserving its content. Previous methods often struggle with content leakage and style misalignment, particularly when using image-driven approaches that aim to transfer precise styles. In this work, we introduce Trajectory Reset Attention Control (TRAC), a novel method that allows for high-quality style transfer while preserving content integrity. TRAC operates by resetting the denoising trajectory and enforcing attention control, thus enhancing content consistency while significantly reducing the computational costs against inversion-based methods. Additionally, a concept termed Style Medium is introduced to bridge the gap between content and style, enabling a more precise and harmonious transfer of stylistic elements. Building upon these concepts, we present a tuning-free framework that offers a stable, flexible, and efficient solution for both image and video style transfer. Experimental results demonstrate that our proposed framework accommodates a wide range of stylized outputs, from precise content preservation to the production of visually striking results with vibrant and expressive styles.
Abstract (translated)
视频风格转换的目标是改变视频的风格，同时保持其内容不变。以往的方法在处理内容渗漏和风格错配的问题上往往效果不佳，特别是在使用以图像驱动的方式进行精确风格转移时更为明显。在这项工作中，我们引入了一种名为轨迹重置注意力控制（Trajectory Reset Attention Control, TRAC）的新方法，该方法能够在保持内容完整性的前提下实现高质量的风格转换。TRAC通过重置去噪路径并强制执行注意力控制来运作，从而增强内容的一致性，并且显著降低了相对于基于逆向的方法的计算成本。 此外，我们还引入了一个名为“样式媒介”的概念，它连接了内容与风格之间的鸿沟，使更精确和谐地传输风格元素成为可能。基于这些概念，我们提出了一种无需调整的框架，提供了一种稳定、灵活且高效的解决方案，适用于图像和视频的风格转换。 实验结果表明，我们的框架能够支持多种多样化的风格化输出，从准确的内容保存到生产出视觉上引人注目的成果，具有生动而富有表现力的风格。
URL
https://arxiv.org/abs/2503.07363
