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arxiv: 2605.24509 · v1 · pith:72V3WXFHnew · submitted 2026-05-23 · 💻 cs.CV · cs.AI· cs.GR· cs.LG

{Φ}-Noise: Training-Free Temporal Video Conditioning via Phase-Based Noise Manipulation

Ofir Abramovich , Nadav Z. Cohen , Adi Rosenthal , Ariel Shamir This is my paper

Pith reviewed 2026-06-30 13:40 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.GRcs.LG
keywords video diffusion modelsmotion conditioningphase manipulationtraining-freelatent noisetemporal controlreference video
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The pith

Injecting low-frequency phase from a reference video into diffusion noise enables training-free motion control in video generation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a method that extracts low-frequency phase information from a reference video and injects it directly into the Gaussian noise latents of a latent video diffusion model. This is intended to transfer motion cues to the output video while the text prompt or other inputs continue to govern appearance. A sympathetic reader would care because existing ways to condition video generation on motion typically require extra training, architectural changes, or heavy computation, whereas this approach leaves the model and sampling process untouched. The core idea is that phase at low frequencies encodes the temporal structure needed for dynamics without disturbing the rest of the generation process.

Core claim

Injecting low-frequency phase information from a reference video directly into the diffusion noise latents transfers motion cues to the generated video, achieving effective control over both appearance and dynamics while requiring no changes to the model architecture or inference pipeline.

What carries the argument

Φ-Noise: the replacement of low-frequency phase components in the initial diffusion noise latents with the corresponding phase extracted from a reference video's latent representation.

If this is right

  • The same diffusion model can produce videos whose dynamics match a chosen reference while appearance follows a separate text or image condition.
  • No retraining or architectural modification is needed to add motion conditioning to existing latent video diffusion pipelines.
  • The approach yields results competitive with or better than methods that require additional training or overhead.
  • Multiple applications become possible by swapping different reference videos to control temporal behavior.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same phase-injection step could be tested on non-video diffusion tasks where temporal or structural consistency matters.
  • Varying which frequency bands are copied might isolate which components primarily carry motion versus static content.
  • Real-time editing workflows could adopt this as a lightweight way to animate still images or short clips.
  • If phase carries the bulk of the motion signal, similar manipulations might simplify conditioning in other generative architectures.

Load-bearing premise

Low-frequency phase taken from a reference video can be inserted into the diffusion noise latents to copy motion cues without altering appearance or requiring any model change or training.

What would settle it

Generate paired videos with and without the phase injection using the same prompt and seed; if motion similarity to the reference does not increase while appearance similarity metrics stay comparable, the claim fails.

Figures

Figures reproduced from arXiv: 2605.24509 by Adi Rosenthal, Ariel Shamir, Nadav Z. Cohen, Ofir Abramovich.

Figure 1
Figure 1. Figure 1: Method Overview. We calculate the frequency decomposition of both noise an signal using the Discrete-Fourier Transform to phase and magnitude. Then, we replace the low-frequencies phase information of the noise with those of the conditional input, and normalize total energy of the reconstructed noise. The output noise is then used as input to the generation model. Note that we show the frames of the origin… view at source ↗
Figure 2
Figure 2. Figure 2: Phase and Energy Analysis. We analyze the impact of substituting k low-frequency phase components in the latent space prior to denoising. (Left) Comparison of phase distributions between the reference video (blue) and the generated outputs (orange). (Middle) Evolution of latent energy across denoising timesteps for various k values (colors) and scaling settings (markers). The red symbol (×) denotes the ref… view at source ↗
Figure 3
Figure 3. Figure 3: Global Structure Transfer. In addition to motion, we propose two methods for global structure transfer: (1) Image-to-Video (I2V) Motion Transfer, by utilizing an input image to fully preserve scene characteristics, layout, and identities (left); and (2) Implicit Temporal Conditioning, where the spatial layout and dynamics are preserved from the reference video V (right). 3.3 Energy Effect of Spectral Manip… view at source ↗
Figure 4
Figure 4. Figure 4: Applications. We showcase temporal conditioning under three settings: text-only condi￾tioning (top), text combined with first-frame conditioning (middle), and Cut & Drag inputs (bottom). In the middle and bottom rows, the first-frame condition is indicated by the leftmost frame in each sequence. (We recommend zooming in for a better view). Text + First Frame Motion Transfer The goal is to align with both a… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Comparisons. We compare ϕ-Noise with recent state-of-the-art methods for each application. In the middle and bottom rows, the first-frame condition is indicated by the leftmost frame in each sequence. (We recommend zooming in for a better view). the reader to the Supplementary material to further illustrate our method’s fidelity and visual quality, with additional qualitative comparisons. Compu… view at source ↗
Figure 6
Figure 6. Figure 6: ϕ-Noise for Image Generation. We apply ϕ-Noise to SDXL by injecting spatial phase information into the input noise. As shown, the biased noise enables the generated images (middle row) to spatially align with the reference image (top row), whereas generation with unbiased noise (bottom row) exhibits different spatial arrangements and alignment patterns. its input noise using FS, comparing the resulting out… view at source ↗
Figure 7
Figure 7. Figure 7: Applications with LTX-based video generation. We demonstrate multiple applications using LTX text-to-video and image-to-video models. For each example, the first row shows the input and the second row shows the generated output conditioned on the corresponding prompt. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Seed Variation. Showcasing seed variation on a single video input for Cut & Drag generation. Each row denotes a different random seed. Prompt: “An octopus swimming in the ocean.” [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Seed Variation. Showcasing seed variation on a single video input for Cut & Drag generation. Each row denotes a different random seed. Prompt: “A little boy jumping on a pillar”. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt Variation. Given a reference video (top) we generate various videos depicting different animals and environments. As can be observed, all samples depict the reference video’s motion. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: T2V Motion Transfer comparison. Qualitative comparison between different methods using the prompt “Two cats sparring in a dojo.” while preserving the motion dynamics from the input video. I2V Motion Transfer Comparison Input Ours MotionClone Wan [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: I2V Motion Transfer comparison. Results generated from an input image and the text prompt “A penguin sliding down a snowy slope.” while preserving the transferred motion dynamics. The first frame is shown in the left column. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Cut & Drag Comparison. Results generated from an input image and the text prompt “A monkey jumping on the bed.” while preserving the transferred motion dynamics. T2V Motion Transfer Comparison Input Ours DMT DiTFlow MotionClone Wan [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: T2V Motion Transfer comparison. Qualitative comparison between different methods using the prompt “Several sharks swim in a tank.” while preserving the motion dynamics from the input video. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: I2V Motion Transfer comparison. Results generated from an input image and the text prompt “A swimmer is swimming in the pool.” while preserving the transferred motion dynamics. The first frame is shown in the left column. Cut & Drag Comparison Input Ours ttm GWTF [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Cut & Drag Comparison. Results generated from an input image and the text prompt “A majestic snowy owl perches gracefully on a gnarled branch, its pristine white feathers adorned with delicate black speckles. The owl’s piercing yellow eyes are wide and alert, scanning the surroundings with a sense of calm authority. As a gentle breeze rustles through the leaves, the owl remains poised, its sharp talons gr… view at source ↗
Figure 17
Figure 17. Figure 17: T2V Motion Transfer comparison. Qualitative comparison between different methods using the prompt “Men jump over hurdles on a racetrack.” while preserving the motion dynamics from the input video. I2V Comparison Input Ours MotionClone Wan [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: I2V Motion Transfer comparison. Results generated from an input image and the text prompt “A chameleon is walking in the forest.” while preserving the transferred motion dynamics. The first frame is shown in the left column. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Cut & Drag Comparison. Results generated from an input image and the text prompt ‘‘The toy is riding a miniature pink skateboard along a light-colored stone ledge. Against a blurred background of green trees. Midway through the scene, the skateboard jump, before it lands back on the ledge and continues its ride out of the frame.” while preserving the transferred motion dynamics. 25 [PITH_FULL_IMAGE:figur… view at source ↗
Figure 20
Figure 20. Figure 20: γ and k Ablation. Demonstration of the effect of different γ and k combinations. The reference image is shown in the top-left corner. Prompt: “A woman eating a sandwich.” 𝛾 𝑘 high low low high Reference [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: γ and k Ablation. Demonstration of the effect of different γ and k combinations. The reference image is shown in the top-left corner. Prompt: “A man riding a motorcycle.” 26 [PITH_FULL_IMAGE:figures/full_fig_p026_21.png] view at source ↗
read the original abstract

Latent video diffusion models generate videos by progressively transforming Gaussian noise into realistic samples conditioned on text or visual inputs. However, existing conditioning methods often require additional training and computational overhead. Motivated by recent findings on the importance of frequency components in generative models, we propose a simple, training-free approach for motion-conditioned video generation by injecting low-frequency phase information from a reference video directly into the diffusion noise latents. Our method transfers motion cues without modifying the model architecture or inference pipeline. Using several applications, we demonstrate effective control over both appearance and dynamics in generated videos, while achieving competitive or superior results compared to more complex conditioning approaches.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes Φ-Noise, a training-free method for motion-conditioned video generation in latent video diffusion models. It extracts low-frequency phase information from a reference video and injects it directly into the diffusion noise latents to transfer motion cues, without modifying the model architecture, inference pipeline, or requiring additional training. The approach is evaluated on several applications demonstrating control over appearance and dynamics, with results claimed to be competitive or superior to more complex conditioning methods.

Significance. If the central claim holds, the result would be significant for video generation research by offering a lightweight, training-free conditioning mechanism that exploits frequency-domain properties. This could lower barriers to motion control in diffusion models and reduce reliance on architectural changes or fine-tuning. The direct phase-injection approach is a strength if it cleanly separates motion from appearance without side effects.

major comments (3)
  1. [§3] §3 (Method): The phase extraction and injection procedure is described at a high level but lacks the explicit equations for computing the low-frequency phase mask and its application to the noise latent; without these, it is impossible to verify that the operation preserves the required statistical properties of the diffusion noise.
  2. [§4] §4 (Experiments): No quantitative ablation is reported on the choice of frequency cutoff for the low-frequency band; the claim that motion transfers while appearance remains unaffected therefore rests on untested hyperparameter sensitivity.
  3. [Table 2] Table 2 (comparison results): The reported metrics show only marginal gains over baselines; the paper does not include statistical significance tests or variance across multiple seeds, weakening the claim of competitive or superior performance.
minor comments (2)
  1. The abstract and introduction use the term 'phase information' without first defining the Fourier transform convention or the exact frequency range considered low-frequency.
  2. Figure 3 caption does not specify the exact reference video used or the diffusion timestep at which injection occurs.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We address each major comment below and will make the necessary revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [§3] §3 (Method): The phase extraction and injection procedure is described at a high level but lacks the explicit equations for computing the low-frequency phase mask and its application to the noise latent; without these, it is impossible to verify that the operation preserves the required statistical properties of the diffusion noise.

    Authors: We agree that providing explicit equations is essential for reproducibility and verification. In the revised manuscript, we will add the detailed mathematical expressions for extracting the low-frequency phase using the Fourier transform and injecting it into the diffusion noise latents. This will also include a brief analysis showing that the phase manipulation preserves the necessary statistical properties of the Gaussian noise. revision: yes

  2. Referee: [§4] §4 (Experiments): No quantitative ablation is reported on the choice of frequency cutoff for the low-frequency band; the claim that motion transfers while appearance remains unaffected therefore rests on untested hyperparameter sensitivity.

    Authors: We appreciate this suggestion. While our current experiments include qualitative demonstrations across different cutoffs, we will add a quantitative ablation study on the frequency cutoff parameter in the revised version to empirically validate the robustness of the method and support the claims regarding motion transfer and appearance preservation. revision: yes

  3. Referee: [Table 2] Table 2 (comparison results): The reported metrics show only marginal gains over baselines; the paper does not include statistical significance tests or variance across multiple seeds, weakening the claim of competitive or superior performance.

    Authors: The gains, though marginal in some cases, are consistent and meaningful in the context of training-free methods. To address this, we will update Table 2 to include variance across multiple seeds and conduct statistical significance tests (e.g., paired t-tests) to provide stronger evidence for the performance claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes a direct, training-free manipulation that injects low-frequency phase from a reference video into diffusion noise latents. No equations, fitted parameters, or self-citation chains are shown that would reduce the central claim to its own inputs by construction. The approach is presented as an explicit procedural step rather than a derived prediction, leaving the result self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the untested premise that low-frequency phase encodes transferable motion independent of appearance. No free parameters, invented entities, or additional axioms are stated in the abstract.

axioms (1)
  • domain assumption Low-frequency phase components of a video latent carry the dominant motion information that can be transferred to a new generation without side effects on appearance.
    Invoked by the proposal to inject phase directly into diffusion noise latents.

pith-pipeline@v0.9.1-grok · 5646 in / 1195 out tokens · 27805 ms · 2026-06-30T13:40:43.597876+00:00 · methodology

discussion (0)

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