Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging
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| Format: | Preprint |
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2025
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| _version_ | 1866916648346189824 |
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| author | Lin, Jiang Yi, Zili |
| author_facet | Lin, Jiang Yi, Zili |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging Lin, Jiang Yi, Zili Computer Vision and Pattern Recognition 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. |
| title | Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07363 |