[Paper] Versatile Editing of Video Content, Actions, and Dynamics without Training

Source: arXiv - 2603.17989v1

Overview

The paper presents DynaEdit, a training‑free technique that lets you edit real‑world videos—changing actions, adding interacting objects, or applying global effects—by leveraging existing pretrained text‑to‑video diffusion models. By sidestepping the need for costly task‑specific training data, DynaEdit opens the door to flexible, high‑quality video manipulation that was previously out of reach for most developers.

Key Contributions

• Training‑free editing pipeline that works with any off‑the‑shelf text‑to‑video diffusion model (model‑agnostic).
• Inversion‑free approach that avoids modifying the internal weights of the pretrained model, preserving its original capabilities.
• Novel stabilization mechanisms that eliminate low‑frequency misalignment and high‑frequency jitter that typically plague naïve adaptations of diffusion‑based video editing.
• Demonstrated ability to edit dynamics, including:
  • Changing human or object actions (e.g., “make the person jump”).
  • Inserting new entities that physically interact with the scene (e.g., “add a ball that bounces off the table”).
  • Applying global scene‑wide transformations (e.g., “turn day into night”).
• State‑of‑the‑art performance on a suite of challenging text‑guided video editing benchmarks, surpassing both trained and other training‑free baselines.

Methodology

1. Base Model Selection – DynaEdit starts with any pretrained text‑to‑video diffusion model that predicts optical flow (the motion field) from a textual prompt.
2. Inversion‑Free Prompt Conditioning – Instead of inverting the video back into the model’s latent space (a costly step in many prior works), DynaEdit directly injects the desired textual prompt into the diffusion process while keeping the original video’s latent representation untouched.
3. Alignment & Jitter Mitigation
   • Low‑frequency misalignment (drift of the whole scene) is corrected by a global motion alignment module that matches the coarse trajectory of the edited flow to the original video.
   • High‑frequency jitter (frame‑to‑frame flicker) is suppressed with a temporal consistency filter that enforces smoothness across consecutive flow fields.
4. Iterative Refinement – The edited flow is rendered back into pixel space using a pretrained video decoder, then re‑fed into the diffusion loop for a few refinement steps, ensuring that newly added objects obey physics and interact plausibly with existing elements.
5. Model‑Agnostic Wrapper – All of the above is implemented as a thin wrapper around the diffusion model, requiring no changes to the model’s weights or architecture.

Results & Findings

• Visual quality: Edited videos retain crisp textures and realistic motion, with no noticeable flicker.
• Temporal coherence: The alignment and jitter modules reduce frame‑wise drift by > 80 % compared to naïve diffusion edits.
• Generalization: Because DynaEdit does not rely on task‑specific fine‑tuning, it works across diverse domains (sports, cooking, indoor scenes) without any extra data.

Practical Implications

• Content creation pipelines – Video editors and motion designers can now script complex edits (“replace the car with a bike that crashes into the wall”) using plain text, dramatically cutting down manual rotoscoping or keyframe animation.
• Game and AR/VR asset generation – Developers can generate or modify short gameplay clips on the fly, inserting interactive props that obey the scene’s physics without writing custom simulation code.
• Automated video personalization – Marketing platforms could automatically adapt stock footage to different audiences (e.g., swapping a person’s gesture or adding a brand logo that interacts with the environment) with a single API call.
• Rapid prototyping for research – Researchers needing custom video scenarios (e.g., “add a moving obstacle”) can generate them without building a bespoke simulator, accelerating data‑collection for downstream tasks like action recognition.

Limitations & Future Work

• Dependence on flow‑based diffusion models – DynaEdit’s quality hinges on the underlying model’s ability to predict accurate optical flow; poorly trained base models will limit edit fidelity.
• Short‑clip focus – The current pipeline is optimized for clips up to a few seconds; scaling to longer sequences may require additional memory‑efficient temporal handling.
• Physical realism constraints – While the method enforces basic motion consistency, it does not incorporate full physics engines, so highly complex interactions (e.g., fluid dynamics) may still look artificial.
• Future directions suggested by the authors include integrating explicit physics priors, extending the framework to 3‑D video (e.g., volumetric capture), and exploring interactive UI tools that let non‑technical users craft prompts in real time.

Authors

• Vladimir Kulikov
• Roni Paiss
• Andrey Voynov
• Inbar Mosseri
• Tali Dekel
• Tomer Michaeli

Paper Information

• arXiv ID: 2603.17989v1
• Categories: cs.CV
• Published: March 18, 2026
• PDF: Download PDF