StreamGVE: Training-Free Video Editing via Few-Step Streaming Video Generation

Problem
This paper addresses the limitations of existing video editing methods, which often require numerous iterations and struggle to produce high-quality results. The authors identify a gap in the literature regarding the compatibility of generative models with the data-to-data paradigm, suggesting that a noise-to-data approach may yield better outcomes. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a novel framework called Streaming-Generation-based Video Editing (StreamGVE), which leverages pre-trained streaming generation models. The architecture incorporates dual-branch fast sampling, utilizing a self-attention bridge to facilitate the integration of source-video conditions. The method employs cross-attention mechanisms for grounding and boosting, which enhance both the sampling process and the conditioning on the source video. Additionally, StreamGVE introduces source-oriented guidance to improve the quality of target generation and a visual prompting strategy to increase editing flexibility. The training compute requirements are not explicitly disclosed, but the method is designed to operate effectively in few-step sampling scenarios.

Results
StreamGVE demonstrates superior performance across various video editing tasks compared to existing methods. The authors report significant improvements in editing quality, even under few-step conditions, which minimizes computational overhead. While specific numerical results and effect sizes are not detailed in the abstract, the paper claims consistent outperformance against named baselines, indicating a robust advantage in both efficiency and output quality.

Limitations
The authors acknowledge that while StreamGVE is effective and generalizable, it may still be constrained by the limitations inherent in the pre-trained models it utilizes. They do not discuss potential issues related to the scalability of the method or its performance on highly complex editing tasks that may require more extensive conditioning. Additionally, the reliance on pre-trained models may limit the adaptability of StreamGVE to novel video editing scenarios that diverge from the training data.

Why it matters
The implications of this work are significant for the field of video editing and generative modeling. By shifting the paradigm from data-to-data to noise-to-data, StreamGVE opens new avenues for efficient video editing that require fewer computational resources and iterations. This approach could lead to advancements in real-time video editing applications and enhance the capabilities of generative models in multimedia content creation. The findings may also inspire further research into hybrid generative techniques that combine the strengths of both paradigms.

Authors: Guanlong Jiao, Chenyangguang Zhang, Jia Jun Cheng Xian, Zewei Zhang, Renjie Liao
Source: arXiv:2605.21466
URL: https://arxiv.org/abs/2605.21466v1