Abstract
Long-form video editing poses unique challenges due to the exponential increase in the computational cost from joint editing and Denoising Diffusion Implicit Models (DDIM) inversion across extended sequences. To address these limitations, we propose PipeFlow, a scalable, pipelined video editing method that introduces three key innovations: First, based on a motion analysis using Structural Similarity Index Measure (SSIM) and Optical Flow, we identify and propose to skip editing of frames with low motion. Second, we propose a pipelined task scheduling algorithm that splits a video into multiple segments and performs DDIM inversion and joint editing in parallel based on available GPU memory. Lastly, we leverage a neural network-based interpolation technique to smooth out the border frames between segments and interpolate the previously skipped frames. Our method uniquely scales to longer videos by dividing them into smaller segments, allowing PipeFlow's editing time to increase linearly with video length. In principle, this enables editing of infinitely long videos without the growing per-frame computational overhead encountered by other methods. PipeFlow achieves up to a 9.6X speedup compared to TokenFlow and a 31.7X speedup over Diffusion Motion Transfer (DMT).
Abstract (translated)
长视频编辑面临着独特的挑战，因为联合编辑和Denoising Diffusion Implicit Models（DDIM）逆向处理在扩展序列上会导致计算成本呈指数级增长。为了应对这些限制，我们提出了PipeFlow，这是一种可扩展的流水线式视频编辑方法，引入了三个关键创新： 首先，基于使用结构相似性指标(SSIM)和光流进行运动分析，我们可以识别并建议跳过低运动帧的编辑。 其次，我们提出了一种流水线任务调度算法，该算法将视频分成多个片段，并根据可用的GPU内存进行DDIM逆向处理和联合编辑操作。这种方法可以在多段上并行执行这些操作。 最后，我们采用基于神经网络的技术来平滑分段间的边界帧，并且插值那些之前被跳过的帧。 我们的方法通过将长视频分割成较小的部分，使其能够独特地扩展到更长时间的视频处理中。这使得PipeFlow的编辑时间线性增加与视频长度有关，原则上可以实现无限长视频的编辑而不会遇到其他方法随帧数增长的计算开销问题。 实验表明，与TokenFlow相比，PipeFlow的速度提高了高达9.6倍；与Diffusion Motion Transfer (DMT)相比，则有31.7倍的加速效果。
URL
https://arxiv.org/abs/2512.24026
