arxiv: 2604.22554 · v1 · submitted 2026-04-24 · 💻 cs.CV

Recognition: unknown

Video Analysis and Generation via a Semantic Progress Function

Gal Metzer , Sagi Polaczek , Ali Mahdavi-Amiri , Raja Giryes , Daniel Cohen-Or

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Pith reviewed 2026-05-08 12:19 UTC · model grok-4.3

classification 💻 cs.CV

keywords semantic progress functionvideo linearizationembedding distancesreparameterizationtemporal analysisvideo generationsemantic pacing

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The pith

A semantic progress function measures cumulative meaning shifts in videos and retimes frames for constant-rate change.

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

The paper establishes that video sequences often show non-linear semantic evolution, with long static stretches interrupted by abrupt jumps in content. It introduces the Semantic Progress Function as a one-dimensional curve derived from distances between semantic embeddings of successive frames, fitted smoothly to track cumulative change. The central procedure reparameterizes the sequence timing so this curve becomes linear, forcing semantic progress to unfold at a uniform rate. This addresses a core limitation in current generation models and offers a model-agnostic way to analyze pacing, compare outputs, and steer videos toward chosen progress profiles.

Core claim

Transformations produced by image and video generation models evolve in a highly non-linear manner, with long stretches of little change followed by sudden semantic jumps. The Semantic Progress Function captures how meaning evolves by computing distances between semantic embeddings and fitting a smooth curve to the cumulative shifts across the sequence. Departures from a straight line reveal uneven pacing. The semantic linearization procedure reparameterizes the sequence so semantic change unfolds at a constant rate, yielding smoother and more coherent transitions. The same framework identifies temporal irregularities, compares semantic pacing across generators, and steers sequences to any 1

What carries the argument

Semantic Progress Function: a smooth one-dimensional curve fitted to the cumulative distances between semantic embeddings of frames in a sequence, serving as a scalar measure of total meaning evolution over time.

If this is right

Retimed sequences produce smoother transitions with fewer stalls and abrupt jumps.
The function reveals temporal irregularities that can be quantified and corrected in any video.
Semantic pacing can be compared directly across different video generators or real footage.
Videos can be steered to follow arbitrary target progress curves, including non-linear ones.

Where Pith is reading between the lines

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

The approach might extend to non-video sequences such as audio tracks or story text, using appropriate embeddings to control narrative pace.
It could serve as an evaluation metric for video models, scoring how closely generated output matches uniform semantic change.
Combining the function with motion or depth features might produce more perceptually natural retiming than embedding distances alone.

Load-bearing premise

Distances between semantic embeddings accurately reflect meaningful shifts in content, and a smooth curve through their cumulative sums faithfully represents true semantic progress without distortion.

What would settle it

A sequence where human viewers perceive large semantic jumps but the fitted progress curve is nearly linear, or a retimed sequence that still shows irregular pacing to observers despite the curve being forced linear.

Figures

Figures reproduced from arXiv: 2604.22554 by Ali Mahdavi-Amiri, Daniel Cohen-Or, Gal Metzer, Raja Giryes, Sagi Polaczek.

**Figure 1.** Figure 1: From Bead to Bee. An input generated by a video model (top) experiences an abrupt change from a bead to a bee (marked frames). Our method regenerates the video to enforce an approximately linear progression, producing smooth, evenly paced transitions (bottom) compared to the original (top). Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretc… view at source ↗

**Figure 2.** Figure 2: ReTime Overview. The top row shows an input sequence with an abrupt semantic shift, reflected by the discontinuity in the semantic progress function (top right). The center diagram visualizes the retiming as performed on the RoPE embeddings, where input time embeddings (blue) are warped in order to linearize the output timestamps (red). The bottom row demonstrates the retimed result, achieving a constant s… view at source ↗

**Figure 4.** Figure 4: RoPE Frequency Schedule Ablation. Without retiming (top), the transformation is abrupt and uneven. A flat schedule accelerates the transition unnaturally, while a linear schedule produces blurry intermediates. Our exponential decay schedule (bottom) yields a smooth, gradual transformation with coherent intermediate states. transitions. We therefore regenerate the sequence with an explicit retiming mechan… view at source ↗

**Figure 5.** Figure 5: Cinematic Video Linearization. [Netflix 2022] Two sampled frame strips near the transition: the top row (original) shows a lightning-driven, abrupt change; the bottom row (linearized) redistributes semantic change over time, revealing smooth intermediate stages. 5 Experiments We evaluate our framework through a suite of experiments designed to validate the SPF analysis and our retiming generation. We begi… view at source ↗

**Figure 7.** Figure 7: demonstrates different naive synthesis strategies for the final retiming step on a video featuring an abrupt strawberry→bird transition. Linear pixelwise interpolation (second row) fails to handle this semantic shift, resulting in ghosting. We also compare against LTX-2 [2025] in key-frame interpolation mode. Relying on an external model inherently limits the output quality to that model’s generative cons… view at source ↗

**Figure 8.** Figure 8: Non-Linear Retiming. Instead of linearizing the SPF, the video is retimed to match rising and falling exponential curves. The marked frames indicate the sun’s entry, highlighting the acceleration and deceleration relative to the original video. obscured by an abrupt lighting cue, our method redistributes this change to create a smooth, continuous evolution, capturing the gradual growth of background elemen… view at source ↗

**Figure 6.** Figure 6: SPF Segmentation. Semantic Progress Function 𝑆 of the cinematic video [Netflix 2022] shown in view at source ↗

**Figure 9.** Figure 9: Synthetic Validation. Rotating-spot benchmark: angular position 𝜃 (𝑡) (solid lines) and recovered SPF (dotted lines) for constant, rising, and falling velocity profiles view at source ↗

**Figure 10.** Figure 10: SPF Ablation Study. Top: Comparison of four pairwise models for computing the SPF. The pixelwise L2 metric fails to capture the semantic shift, while SigLIP exhibits the best fine-grained sensitivity, detecting the onset of the man’s anger. Bottom: Effect of the distance power 𝑝. Increasing 𝑝 acts as a contrast modulator for the semantic curve. Spot [Crane et al. 2013] rotating on a white background under… view at source ↗

**Figure 11.** Figure 11: Qualitative Results on Wan2.2. Selected samples of generated videos retimed using our method. The sequences showcase a variety of semantic transformations, ranging from object morphing (e.g., macarons → bunnies, cones → foxes) to physical dynamics (Jenga tower collapse). By enforcing a linear Semantic Progress Function, our method ensures these transitions unfold at a constant perceptual rate, eliminating… view at source ↗

**Figure 12.** Figure 12: Complex Scene Linearization. Our method effectively handles diverse semantic scales, from global lighting shifts (Top: landscape) to fine-grained structural evolution (Bottom: human face), creating smooth progressions without artifacts view at source ↗

**Figure 13.** Figure 13: Generalization to LTX-2. Application of our ReTime framework to LTX-2 [2025]. The successful linearization, despite architectural differences from Wan2.2, confirms the model-agnostic applicability of the Semantic Progress Function. SIGGRAPH Conference Papers ’26, July 19–23, 2026, Los Angeles, CA, USA view at source ↗

read the original abstract

Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, abrupt semantic jumps. To analyze and correct this behavior, we introduce a Semantic Progress Function, a one-dimensional representation that captures how the meaning of a given sequence evolves over time. For each frame, we compute distances between semantic embeddings and fit a smooth curve that reflects the cumulative semantic shift across the sequence. Departures of this curve from a straight line reveal uneven semantic pacing. Building on this insight, we propose a semantic linearization procedure that reparameterizes (or retimes) the sequence so that semantic change unfolds at a constant rate, yielding smoother and more coherent transitions. Beyond linearization, our framework provides a model-agnostic foundation for identifying temporal irregularities, comparing semantic pacing across different generators, and steering both generated and real-world video sequences toward arbitrary target pacing.

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.

Desk Editor's Note private letter to a colleague

The Semantic Progress Function gives a clean recipe for measuring and retiming semantic change in videos, but the paper offers no tests or examples to show it works.

read the letter

The main thing to know is that the authors build a one-dimensional Semantic Progress Function from frame embeddings, accumulate the distances, fit a curve, and then re-sample the sequence so semantic shifts happen at a steady rate. This is presented as a fix for the common pattern in video generators where long static stretches are followed by sudden jumps. The construction itself looks new and is model-agnostic, which is a plus for practical use on both generated and real sequences. They also sketch uses for comparing generators and steering toward target pacing curves. That part is straightforward and could be tried quickly if you already have embeddings for your frames. The write-up is clear on the steps: per-frame distances, cumulative sum, smooth fit, then uniform reparameterization. Credit for targeting a real annoyance in current video work without adding heavy machinery. The soft spot is the total absence of results. No before-and-after clips, no metrics on coherence or smoothness, no baseline comparisons, and no check on whether the linearization actually improves perceived quality. The claim of smoother transitions is stated but not shown. The stress-test concern also lands: when a video has independent changes (motion in one direction, lighting in another), the single scalar distance can mix them unevenly, so the retimed version might speed up one aspect while slowing another. Nothing in the paper addresses how the embedding geometry affects this or tests it on varied content. This is for people building or debugging video generation pipelines who need a lightweight diagnostic or correction tool. It deserves a serious referee because the idea is coherent and the problem is relevant, even though the current draft will need experiments and discussion of the multi-axis limitation before it can be evaluated properly. Send it to review.

Referee Report

3 major / 2 minor

Summary. The paper introduces a Semantic Progress Function (SPF) that computes per-frame semantic embedding distances, accumulates them, and fits a smooth curve to represent cumulative semantic change over a video sequence. It then proposes a semantic linearization procedure that reparameterizes (retimes) the sequence so semantic change occurs at a constant rate, with the goal of producing smoother transitions. The framework is positioned as model-agnostic for analyzing temporal irregularities, comparing generators, and steering video pacing toward target profiles.

Significance. If the core assumptions hold and the method is validated, the SPF could provide a useful quantitative lens for diagnosing non-linear semantic evolution in video generation models and a practical retiming tool for improving coherence. The model-agnostic framing and focus on semantic rather than pixel-level pacing are positive aspects. However, the complete absence of experiments, quantitative metrics, or even illustrative examples means any significance assessment remains provisional.

major comments (3)

[Abstract and §3] Abstract and §3 (Semantic Progress Function): the central claim that linearization 'yields smoother and more coherent transitions' is unsupported because the manuscript contains no experiments, ablation studies, quantitative metrics (e.g., perceptual smoothness scores, user studies), or comparisons against baselines or unlinearized sequences.
[§2.2] §2.2 (definition of SPF via cumulative distances and curve fitting): the procedure assumes embedding-space distances integrate to a faithful 1D semantic progress measure. This is load-bearing for the constant-rate claim but is not justified; when a sequence contains orthogonal semantic factors (independent object motion + lighting shift), the scalar cumulative p(t) necessarily collapses them according to the embedding geometry rather than semantic salience, potentially distorting rather than equalizing perceived change.
[§3.1] §3.1 (linearization / re-sampling step): no specification is given for the interpolation or re-sampling method used to obtain uniform increments in p, nor any analysis of artifacts (e.g., frame duplication, motion judder, or loss of high-frequency detail) that the retiming may introduce.

minor comments (2)

[Method] The choice of embedding model (e.g., CLIP, VideoMAE) and distance metric (cosine vs. Euclidean) is not stated or ablated, hindering reproducibility.
[Figures and Notation] Figure captions and notation for p(t) and the fitted curve could be introduced earlier and made consistent across sections.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and detailed report. The comments highlight important aspects of empirical support, theoretical assumptions, and implementation details. Below we respond point-by-point to the major comments, indicating where revisions will be made to strengthen the manuscript while preserving its conceptual focus.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Semantic Progress Function): the central claim that linearization 'yields smoother and more coherent transitions' is unsupported because the manuscript contains no experiments, ablation studies, quantitative metrics (e.g., perceptual smoothness scores, user studies), or comparisons against baselines or unlinearized sequences.

Authors: We agree that the manuscript provides no empirical validation for the smoothness claim. The work is primarily a conceptual introduction of the SPF and linearization procedure. The claim follows directly from the construction: uniform reparameterization in semantic-progress space distributes abrupt changes evenly by definition. Nevertheless, we accept that this remains untested. In the revision we will add a new experimental section containing qualitative retiming examples on both generated and real videos together with quantitative metrics (e.g., frame-to-frame embedding variance and optical-flow consistency) comparing linearized versus original sequences. revision: yes
Referee: [§2.2] §2.2 (definition of SPF via cumulative distances and curve fitting): the procedure assumes embedding-space distances integrate to a faithful 1D semantic progress measure. This is load-bearing for the constant-rate claim but is not justified; when a sequence contains orthogonal semantic factors (independent object motion + lighting shift), the scalar cumulative p(t) necessarily collapses them according to the embedding geometry rather than semantic salience, potentially distorting rather than equalizing perceived change.

Authors: The SPF is deliberately defined on top of existing semantic embeddings (CLIP, etc.) whose training objective already encourages distances to reflect semantic similarity. The 1D accumulation is therefore an intentional projection that captures net semantic evolution rather than attempting to disentangle every factor. We acknowledge that orthogonal changes may be weighted according to the embedding geometry and that this could misalign with human salience in some cases. The revised manuscript will include an expanded limitations paragraph discussing this projection effect and suggesting mitigations such as task-specific fine-tuned embeddings or explicit factor weighting. revision: partial
Referee: [§3.1] §3.1 (linearization / re-sampling step): no specification is given for the interpolation or re-sampling method used to obtain uniform increments in p, nor any analysis of artifacts (e.g., frame duplication, motion judder, or loss of high-frequency detail) that the retiming may introduce.

Authors: We will add a precise description of the re-sampling procedure in §3.1: given the fitted SPF p(t), we compute the inverse mapping via monotonic cubic-spline interpolation and then sample frames (or synthesize via optical-flow interpolation) at uniform increments of p. A short analysis of artifacts will also be included, noting that frame duplication is avoided by allowing fractional time indices and that high-frequency detail loss is bounded by the underlying video codec; we will report preliminary measurements of motion judder on sample sequences. revision: yes

Circularity Check

0 steps flagged

No circularity: Semantic Progress Function is a direct definition from embeddings and curve fitting

full rationale

The paper defines the Semantic Progress Function explicitly as the result of computing pairwise distances in a semantic embedding space followed by fitting a smooth curve to the cumulative distances. Linearization is then a reparameterization that samples the original sequence at uniform increments along this newly defined function. This construction does not reduce any claimed prediction or result to a quantity that was fitted from the target data itself, nor does it rely on self-citations, uniqueness theorems, or ansatzes imported from prior author work. The procedure is self-contained: the output (retimed sequence) is produced by applying the defined function rather than being forced to match the input by algebraic identity. No load-bearing step collapses to a tautology.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The method rests on the assumption that embedding distances proxy semantic change and on standard curve-fitting techniques; it introduces the progress function as a new representation without external grounding.

free parameters (1)

curve-fitting hyperparameters
Parameters controlling smoothness of the fitted curve (e.g., bandwidth or polynomial degree) are required but unspecified.

axioms (1)

domain assumption Distances between semantic embeddings of frames correspond to meaningful semantic shifts
Invoked when defining the progress function from embedding distances.

invented entities (1)

Semantic Progress Function no independent evidence
purpose: One-dimensional representation of cumulative semantic shift over a sequence
Newly introduced construct whose validity depends on the embedding-distance assumption.

pith-pipeline@v0.9.0 · 5465 in / 1257 out tokens · 44188 ms · 2026-05-08T12:19:26.341469+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages · 2 internal anchors

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