Versatile Editing of Video Content, Actions, and Dynamics without Training

2026-03-18

AI Summary

Existing video editing methods struggle to flexibly modify actions, dynamic events, or insert content that influences scene interactions in real-world videos, often constrained by scarce training data or limited editing capabilities. This work proposes DynaEdit, a training-free, general-purpose video editing framework built upon pretrained text-to-video diffusion models. Without requiring model inversion or architectural modifications, DynaEdit introduces a novel temporal alignment and stability control mechanism that enables complex editing tasks—such as action modification, insertion of interactive objects, and addition of global dynamic effects—for the first time. The method effectively mitigates low-frequency misalignment and high-frequency flickering artifacts, achieving state-of-the-art performance across diverse text-driven video editing benchmarks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Controlled video generation has seen drastic improvements in recent years. However, editing actions and dynamic events, or inserting contents that should affect the behaviors of other objects in real-world videos, remains a major challenge. Existing trained models struggle with complex edits, likely due to the difficulty of collecting relevant training data. Similarly, existing training-free methods are inherently restricted to structure- and motion-preserving edits and do not support modification of motion or interactions. Here, we introduce DynaEdit, a training-free editing method that unlocks versatile video editing capabilities with pretrained text-to-video flow models. Our method relies on the recently introduced inversion-free approach, which does not intervene in the model internals, and is thus model-agnostic. We show that naively attempting to adapt this approach to general unconstrained editing results in severe low-frequency misalignment and high-frequency jitter. We explain the sources for these phenomena and introduce novel mechanisms for overcoming them. Through extensive experiments, we show that DynaEdit achieves state-of-the-art results on complex text-based video editing tasks, including modifying actions, inserting objects that interact with the scene, and introducing global effects.

Problem

• video editing
• action modification
• dynamic events
• training-free
• object interaction

Innovation

• training-free video editing
• text-to-video diffusion models
• dynamic event modification
• model-agnostic editing
• video content insertion