title: FlowDirector: Training-Free Flow Steering for Precise Text-to-Video
    Editing
summary: Text-driven video editing aims to modify video content according to natural
  language instructions. While recent training-free approaches have made progress
  by leveraging pre-trained diffusion models, they typically rely on
  inversion-based techniques that map input videos into the latent space, which
  often leads to temporal inconsistencies and degraded structural fidelity. To
  address this, we propose FlowDirector, a novel inversion-free video editing
  framework. Our framework models the editing process as a direct evolution in
  data space, guiding the video via an Ordinary Differential Equation (ODE) to
  smoothly transition along its inherent spatiotemporal manifold, thereby
  preserving temporal coherence and structural details. To achieve localized and
  controllable edits, we introduce an attention-guided masking mechanism that
  modulates the ODE velocity field, preserving non-target regions both spatially
  and temporally. Furthermore, to address incomplete edits and enhance semantic
  alignment with editing instructions, we present a guidance-enhanced editing
  strategy inspired by Classifier-Free Guidance, which leverages differential
  signals between multiple candidate flows to steer the editing trajectory toward
  stronger semantic alignment without compromising structural consistency.
  Extensive experiments across benchmarks demonstrate that FlowDirector achieves
  state-of-the-art performance in instruction adherence, temporal consistency,
  and background preservation, establishing a new paradigm for efficient and
  coherent video editing without inversion.
paper_authors._id: 684309118f9ec8394c514861
paper_authors.name: Guangzhao Li
paper_authors._id: 684309118f9ec8394c514862
paper_authors.name: Yanming Yang
paper_authors._id: 684309118f9ec8394c514863
paper_authors.name: Chenxi Song
paper_authors._id: 684309118f9ec8394c514864
paper_authors.name: Chi Zhang
paper_title: FlowDirector: Training-Free Flow Steering for Precise Text-to-Video
    Editing
paper_summary: Text-driven video editing aims to modify video content according to natural
  language instructions. While recent training-free approaches have made progress
  by leveraging pre-trained diffusion models, they typically rely on
  inversion-based techniques that map input videos into the latent space, which
  often leads to temporal inconsistencies and degraded structural fidelity. To
  address this, we propose FlowDirector, a novel inversion-free video editing
  framework. Our framework models the editing process as a direct evolution in
  data space, guiding the video via an Ordinary Differential Equation (ODE) to
  smoothly transition along its inherent spatiotemporal manifold, thereby
  preserving temporal coherence and structural details. To achieve localized and
  controllable edits, we introduce an attention-guided masking mechanism that
  modulates the ODE velocity field, preserving non-target regions both spatially
  and temporally. Furthermore, to address incomplete edits and enhance semantic
  alignment with editing instructions, we present a guidance-enhanced editing
  strategy inspired by Classifier-Free Guidance, which leverages differential
  signals between multiple candidate flows to steer the editing trajectory toward
  stronger semantic alignment without compromising structural consistency.
  Extensive experiments across benchmarks demonstrate that FlowDirector achieves
  state-of-the-art performance in instruction adherence, temporal consistency,
  and background preservation, establishing a new paradigm for efficient and
  coherent video editing without inversion.
paper_ai_summary: FlowDirector, an inversion-free video editing framework, uses ODEs for spatiotemporal coherent editing and attention-guided masking for localized control, achieving state-of-the-art performance.
paper_ai_keywords: text-driven video editing diffusion models inversion-free framework Ordinary Differential Equation (ODE) spatiotemporal manifold attention-guided masking Classifier-Free Guidance semantic alignment instruction adherence temporal consistency background preservation