In video editing, the hallmark of a quality edit lies in its consistent and
unobtrusive adjustment. Modification, when integrated, must be smooth and
subtle, preserving the natural flow and aligning seamlessly with the original
vision. Therefore, our primary focus is on overcoming the current challenges in
high quality edit to ensure that each edit enhances the final product without
disrupting its intended essence. However, quality deterioration such as
blurring and flickering is routinely observed in recent diffusion video editing
systems. We confirm that this deterioration often stems from high-frequency
leak: the diffusion model fails to accurately synthesize high-frequency
components during denoising process. To this end, we devise Frequency Adapting
Group (FRAG) which enhances the video quality in terms of consistency and
fidelity by introducing a novel receptive field branch to preserve
high-frequency components during the denoising process. FRAG is performed in a
model-agnostic manner without additional training and validates the
effectiveness on video editing benchmarks (i.e., TGVE, DAVIS).

本研究重点关注视频编辑中的高质量调整，提出了一种名为 Frequency Adapting Group (FRAG) 的方法，通过引入一个新的接受域分支，在去噪过程中保留高频分量，从而提高视频编辑的一致性和保真度。该方法在 TGVE 和 DAVIS 等视频编辑基准上得到了验证。