FlowDirector: Training-Free Flow Steering for Precise Text-to-Video Editing
Guangzhao Li, Yanming Yang, Chenxi Song, Chi Zhang

TL;DR
FlowDirector introduces a training-free, inversion-free framework for precise text-driven video editing that models editing as a direct evolution in data space, ensuring high fidelity and temporal consistency.
Contribution
It proposes a novel inversion-free, data-space evolution approach with flow correction strategies, advancing the state-of-the-art in training-free video editing.
Findings
Achieves state-of-the-art instruction following accuracy.
Ensures high temporal consistency and background preservation.
Demonstrates effectiveness across various editing tasks.
Abstract
Text-driven video editing aims to modify video content based on natural language instructions. While recent training-free methods have leveraged pretrained diffusion models, they often rely on an inversion-editing paradigm. This paradigm maps the video to a latent space before editing. However, the inversion process is not perfectly accurate, often compromising appearance fidelity and motion consistency. To address this, we introduce FlowDirector, a novel training-free and inversion-free video editing framework. Our framework models the editing process as a direct evolution in the data space. It guides the video to transition smoothly along its inherent spatio-temporal manifold using an ordinary differential equation (ODE), thereby avoiding the inaccurate inversion step. From this foundation, we introduce three flow correction strategies for appearance, motion, and stability: 1)…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Video Analysis and Summarization · Multimodal Machine Learning Applications
MethodsDiffusion
