Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging

• URL: http://arxiv.org/abs/2503.07363v1
• Date: Mon, 10 Mar 2025 14:18:43 GMT
• Title: Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging
• Authors: Jiang Lin, Zili Yi,
• Abstract summary: We introduce Trajectory Reset Attention Control (TRAC), a novel method that allows for high-quality style transfer.<n>TRAC operates by resetting the denoising trajectory and enforcing attention control, thus enhancing content consistency.<n>We present a tuning-free framework that offers a stable, flexible, and efficient solution for both image and video style transfer.
• Score: 5.501345898413532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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