Source-linked AI summary
Versatile Editing of Video Content, Actions, and Dynamics without Training
Vladimir Kulikov, Roni Paiss, Andrey Voynov, Inbar Mosseri, Tali Dekel, Tomer Michaeli
TL;DR
Editing actions, interactions, and dynamics in real-world videos remains difficult because existing methods and training data are limited. DynaEdit provides a training-free, model-agnostic approach built on inversion-free editing with pretrained text-to-video flow models. It achieves state-of-the-art results on complex text-based edits, while retaining limitations inherited from the underlying I2V model and from very large modifications.
Problem
Editing actions and dynamic interactions in real-world videos remains challenging because existing trained models struggle with complex edits and suitable paired training data is difficult to obtain.
Method
DynaEdit steers a pretrained text-to-video flow model through an inversion-free, training-free editing process without intervening in model internals.
Results
DynaEdit achieves state-of-the-art results on complex editing tasks and performance at least comparable to the only existing trained model.
Takeaways & Limitations
The method supports text-based action modification, interacting object insertion, and global effects while preserving video properties unrelated to the edit.
Takeaways & Limitations
DynaEdit inherits I2V-model physics limitations and often fails to make very large spatio-temporal modifications while preserving unaffected regions.
Abstract
from arXiv · showhide
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.
1 Introduction
DynaEdit addresses the open challenge of editing non-rigid dynamics and interactions in real-world videos without requiring difficult paired training data. It uses a training-free flow-based approach to alter actions, interacting contents, and global effects while preserving unaffected video properties.
- Editing non-rigid dynamics in real-world videos remains difficult because suitable paired source-target training data is exceptionally hard to collect or simulate at scale.
- DynaEdit is a training-free method that steers a pretrained text-to-video flow model using an input video and target text prompt.
- DynaEdit modifies scene dynamics and preserves properties that should remain unaffected by the requested edit.
- The method supports action changes, interacting object insertion, and global modifications such as changing a sunny scene into nighttime.
- DynaEdit combines inversion-free editing with mechanisms addressing low-frequency source misalignment and high-frequency jitter caused by naive adaptation to large spatio-temporal edits.
2 Related Work
Video editing methods span specialized edits, trained general editing, and training-free approaches, but current methods remain limited in handling complex dynamic interactions. Training-free inversion-based insertion can look natural yet cannot alter surrounding scene outcomes, while inversion-free methods provide an alternative path.
- Existing video-editing methods target specific edits, including motion, effects, object insertion, controlled motion, re-angling, and style transfer.
- General video editing requires substantial data collection and computational resources, while lightweight inference-time training offers a partial alternative.
- RunwayML’s Gen-4 Aleph is the only trained model reported to support in-the-wild video editing, but it still struggles with complex manipulations.
- Training-free methods using pretrained video flow models divide broadly into inversion-based and inversion-free approaches.
- DynVFX can insert objects harmoniously, but inserted objects cannot dynamically interact with the surrounding scene or alter video outcomes.
3 Preliminaries
The preliminaries describe flow-based video generation and FlowEdit’s inversion-free transformation from a source video to an edited video. FlowEdit’s initialization timestep controls how much noise is added and therefore the tradeoff between edit expressivity and structural adherence.
- Flow Models: Flow models generate samples by numerically solving a learned velocity-field ODE from Gaussian noise at t = 1 to the data distribution at t = 0.
- Rectified Flows: Rectified flows use a linear interpolation between statistically independent X_0 and X_1, yielding low-curvature paths that support few discretization steps.
- I2V Models: The I2V model conditions its velocity field on a text prompt and first-frame image, enabling sampling from the conditional distribution of videos given those inputs.
- FlowEdit: FlowEdit accepts a source video, source prompt, target prompt, and optionally an edited first frame, then outputs an edited video through inversion-free editing.
- FlowEdit: FlowEdit constructs a noise-free ODE that directly transforms the source video into an edited video using source- and target-conditioned velocities.
- FlowEdit: The method approximates its velocity expectation by averaging independent noise samples at each timestep, with n_avg often set to 1.
- FlowEdit: The initialization timestep n_max controls the maximum source deviation and the tradeoff between edit expressivity and structural adherence.
4 Roadblocks towards motion and interaction editing
Complex motion and interaction edits expose a trade-off between prompt adherence, source-video alignment, and temporal stability. Inversion-free editing suffers from low-frequency misalignment and high-frequency jitter, motivating DynaEdit’s noise-free path, SGA, and ANC mechanisms.
- Failure modes: FlowEdit cannot practically balance edit strength, output quality, prompt adherence, and source-video loyalty for major spatio-temporal modifications.With nmax = N −1, the horse fails to jump; with nmax = N, extraneous trajectory changes and severe jitter appear.
- Low-frequency misalignment: Different initial noise samples alter coarse features such as camera motion, train position, train speed, and bucket explosion timing.These changes are unnecessary for satisfying the edit prompt but arise because the initial step has strong influence on coarse spatio-temporal structure.
- High-frequency jitter: Independent noise samples across timesteps produce high-frequency jitter in inserted or dynamically edited assets.The bucket and paint drops become fuzzy because uncorrelated noise steers consecutive edit velocities in different directions.
- Trade-off: Using one noise realization across all timesteps removes jitter but worsens coarse alignment, producing unnatural interactions such as a levitating bucket.The opposing effects suggest that partial noise correlation may balance visual quality and low-frequency alignment.
- DynaEdit: DynaEdit addresses these problems with a noise-free source-to-edit path, Similarity Guided Aggregation, and Annealed Noise Correlation.The SGA and ANC components are presented as mechanisms for controlling alignment and temporal noise consistency.
5 Method
DynaEdit combines similarity-guided selection of edit paths with progressively correlated noise to preserve source alignment while supporting substantial dynamic changes. Its procedure initializes source and target noisy samples, aggregates edit velocities, and propagates a noise-free path from source to edited video.
- Method overview: DynaEdit is an inversion-free method that uses two components to overcome complex-edit limitations.The method is illustrated as a noise-free path from the source video to the edited video.
- Method overview: The algorithm accepts a real source video and source and target prompts, then returns an edited video.The pseudocode labels these as xsrc, csrc, ctar, and xedit.
- Annealed Noise Correlation: At each timestep, ANC constructs correlated noise, noisy source samples, and noisy target samples before calculating velocity differences.The pseudocode samples random noise, applies the ANC update, and constructs source samples from xsrc and the timestep value.
- Method overview: The aggregated edit velocity propagates the noise-free path until the algorithm returns zedit as the edited video.The pseudocode updates zedit using the timestep difference and the aggregated velocity.
- Similarity Guided Aggregation: SGA predicts candidate final edits, scores their cosine similarity to the source video, and uses normalized weights to aggregate their edit directions.The projected edits are formed using zedit − tiV^Δ_j, then transformed into a combined velocity.
- Similarity Guided Aggregation: SGA uses multiple candidate paths mainly during the first few timesteps to reduce computation while preserving stronger source alignment when temperature is small.As τ decreases, the aggregation approaches hard selection of the edit path that best matches the source video.
- Annealed Noise Correlation: ANC increases noise correlation toward the last sampling steps, where high-frequency jitter is most prominent.The schedule uses atN = 0 and at1 = 1, transitioning from independent samples toward greater correlation.
6 Experiments
DynaEdit is evaluated on a curated benchmark spanning diverse dynamic and interactive edits, with qualitative and quantitative comparisons against training-free and trained baselines. The results show strong preservation of unrelated content, plausible dynamics, and competitive overall quality.
- 6.2 Evaluation set: The evaluation set contains 71 video-editing tuples across interactive insertion, object swapping, motion or action modification, and global effects.Each category contains at least 15 edits; source clips are 49–81 frames at 832 × 480 resolution and 16fps.
- 6.3 Qualitative results: DynaEdit realistically manipulates motion, actions, and global effects while preserving camera motion, unrelated object actions, and source motion patterns when they are not targeted.Examples include making a ball enter a pocket, making a horse jump, and changing global scene appearance.
- 6.5 Qualitative comparisons: In the strawberry-to-feather edit, DynaEdit produces plausible feather motion and flotation while preserving the left strawberry’s dynamics, unlike competing methods.Other methods either make the left strawberry disappear or fade, or change its velocity.
- 6.6 Quantitative comparisons: VLM ratings place DynaEdit first in content preservation, with text adherence and visual quality comparable to the trained Aleph model.The evaluation scores adherence to the source video, adherence to the target text, and overall visual quality on a 1-to-5 scale.
- 6.6 Quantitative comparisons: A user study reports higher preference rates for DynaEdit than the top three competing methods, with Aleph the closest competitor.The comparisons include Runway Aleph, FlowEdit, and I2V sampling.
7 Conclusion
DynaEdit is presented as a training-free framework for substantially modifying video dynamics and content while remaining competitive with the only existing trained model. The authors report state-of-the-art results, while identifying inherited model limitations and failures on very large modifications with preservation requirements.
- 7 Conclusion: DynaEdit enables significant modification of video dynamics and contents through a training-free framework.The method extends the inversion-free paradigm beyond its earlier use for structure-preserving edits.
- 7 Conclusion: The method achieves performance at least comparable to the only existing trained model for in-the-wild video editing.The conclusion identifies DynaEdit as the first training-free method addressing this task.
- 7 Conclusion: DynaEdit achieves state-of-the-art results on complex editing tasks.The conclusion bases this claim on extensive experiments.
- 7 Conclusion: DynaEdit inherits limitations of its underlying I2V model, including difficulty with physics and resulting artifacts.The method can also fail when very large spatio-temporal modifications must preserve unaffected regions.
Appendices
The appendices provide additional examples, model-transfer results, and qualitative baseline comparisons. These materials illustrate DynaEdit’s category coverage and its adaptability across I2V models.
- Supplementary materials: The paper recommends the project website for video data, results, and comparison videos.The website supplements the appendix’s textual and figure-based evidence.
- Additional results: Additional WAN 2.1 results are organized into interactive insertion, interactive swap, action change, and global effects.The videos cover diverse scenarios.
- A.2 Additional results with the Hunyuan model: DynaEdit adapts to the Hunyuan 1.5 I2V model and performs dynamic edits with good source-video and target-prompt adherence.This demonstrates the method’s model-agnostic behavior on an additional I2V model.
- Qualitative comparisons: Appendix comparisons include FlowEdit, I2V sampling, SDEdit, Editing-by-ODE-Inversion, FlowAlign, and Runway Aleph.These are presented as additional qualitative comparisons against competing methods.
B.2 VLM evaluation - table
The evaluation compares DynaEdit with training-free and trained methods across dynamic insertion and object-swap edits. DynaEdit preserves source content while following prompts and producing visually plausible interactions.
- Object insertion: DynaEdit integrates inserted objects with scene dynamics, including lighting and wind reactions, whereas DynVFX shows limited interaction.Examples include a helicopter illuminating a city and a kite or umbrella reacting to wind.
- Object insertion: DynVFX achieves good visual quality but receives lower text adherence because inserted objects cannot affect scene outcomes.
- Quantitative evaluation: DynaEdit outperforms training-free methods across insertion, swap, action-change, and global-effect categories on visual quality, content preservation, and text adherence.Against Runway Aleph, it performs better on dynamic insertion and object swap, comparably on action change, and worse only on global effects.
- Object swapping: DynaEdit performs interactive object swaps that require scene-wide reactions, while FlowDirector’s localized edits fail to add meaningful reaction effects.The lemon-to-green-juice edit includes a chemical reaction that FlowDirector does not meaningfully generate.
- Quantitative evaluation: DynaEdit achieves a favorable balance of source preservation, target-text adherence, and visual quality across edit categories against training-free methods.
- Ablations: SGA preserves source object dynamics and camera motion better than regular averaging, while ANC reduces high-frequency jitter by correlating edit velocities.ANC produces more correlated consecutive velocities than i.i.d. noise, especially later in the edit path.
- Ablations: The Markovian increasing noise-correlation schedule avoids ghosting and high-frequency jitter, unlike non-Markovian or mirrored schedules.
C.4 SGA similarity function ablation
The SGA ablation compares cosine and negative-MSE similarity losses, while related robustness tests examine prompt phrasing and noise-correlation choices. Cosine similarity especially helps preserve delicate fine motion.
- SGA similarity functions: Cosine similarity preserves delicate source motion better than negative MSE when fine spatio-temporal motion is present.In the paintbrush-to-pencil edit, cosine similarity better aligns the paintbrush motion; bird-to-phoenix results show little difference.
- Prompt robustness: Prompt phrasing has a marginal effect when alternative source-target prompts convey the same meaning.
- Noise-correlation schedules: The Markovian increasing noise-correlation schedule produces artifact- and jitter-free edits, unlike non-Markovian and mirrored schedules.Non-Markovian correlations produce ghosting, while lower later-step correlations produce high-frequency jitter.
- Image-to-video conditioning: Image-to-video conditioning confines edits more closely to intended subjects while preserving scene colors and background compared with text-to-video conditioning.In the horse-jumping example, the I2V variant mainly changes the horse, whereas the T2V variant changes colors and background.
F VLM Evaluation Protocol
The VLM protocol evaluates edited videos against their source and target prompts using separate assessments of source adherence, edit adherence, and visual quality. A 1–5 scale is used for each criterion.
- Protocol: The VLM receives source and edited videos and scores source adherence, edit adherence, and visual quality on a 1–5 scale.Source adherence covers unnecessary changes; edit adherence covers fulfilling the requested interaction or outcome; visual quality covers artifacts and flicker.
- Source adherence: Source adherence measures whether unnecessary changes to camera trajectories, object motions, and other source content are minimized.
- Edit adherence: Edit adherence measures whether the result fulfills the target instruction, including logical interactions and outcomes.
- Visual quality: Visual quality evaluates blur, artifacts, flickering, and temporal consistency, including whether objects change unreasonably over time.
- User study: The user study presents 18 video sets in randomized side-by-side comparisons between the proposed method and a baseline.
G.2 Evaluation criteria
The evaluation asks users to compare edited videos along three dimensions: technical visual quality, preservation of the source, and execution of the target edit.
- Evaluation criteria: Participants assess visual quality by judging artifacts, flickering, and overall realism.
- Evaluation criteria: Source loyalty measures preservation of the background, structure, and original motion.
- Evaluation criteria: Target adherence measures how effectively the method executes the edit described in the target prompt.
H Limitations
DynaEdit’s limitations include dependence on hyperparameter choices and constraints inherited from the base I2V model. These constraints can reduce visual quality or produce unrealistic temporal interactions.
- Base-model limitations: Limited-capacity open-source video models can produce sub-optimal visual quality and unrealistic temporal interactions.Examples include distorted faces, low-quality generated monsters, inconsistent hats, and static palm trees during storm edits.
- Base-model limitations: Introducing new global effects may leave source objects unchanged, producing unrealistic interactions with the edited environment.In one example, palm trees remain static despite the introduction of stormy weather.
- Hyperparameter sensitivity: Favorable edits sometimes depend on hyperparameter configuration, which trades off source preservation, edit adherence, and visual quality.The paper illustrates these configuration-dependent effects in Figure 16.
- Hyperparameter sensitivity: When a horse occupies many pixels, stronger classifier-free guidance is preferred because weaker settings may under-edit it and create visual artifacts.Figure 16 identifies parameter sets (3) and (4) as preferable for this case.