Source-linked AI summary
Video Analysis and Generation via a Semantic Progress Function
Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri, Raja Giryes, Daniel Cohen-Or
TL;DR
Generated and real-world video transformations can change meaning unevenly, with static stretches followed by abrupt semantic jumps. The paper introduces the Semantic Progress Function to measure cumulative semantic change and semantic linearization to retime sequences at a constant semantic rate, producing smoother and more predictable transformations without retraining or manual annotation.
Problem
Video sequences often exhibit uneven semantic pacing, while prior methods lack a principled measure of semantic-change rates, abrupt shifts, and cross-model pacing.
Method
The paper computes framewise semantic distances, fits the one-dimensional Semantic Progress Function, and reparameterizes time using SPF-derived retiming.
Results
Semantic linearization produces smoother and more predictable transformations, and the framework is validated across synthetically generated and real-world video sequences.
Takeaways & Limitations
The framework provides a model-agnostic basis for measuring semantic pacing and correcting uneven evolution without model retraining or manual annotation.
Takeaways & Limitations
Frame-level embeddings can reflect camera motion, lighting, and other non-semantic appearance changes, while excessive iterative refinement may degrade output quality.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper addresses uneven semantic evolution in generated and real-world video, introducing the Semantic Progress Function and semantic linearization to measure and correct semantic pacing.
- Generated sequences can remain semantically static before abrupt jumps, undermining perceptual coherence, controllability, and downstream editing.
- Prior methods do not principledly quantify semantic-change rates, locate abrupt shifts, or compare pacing across models.
- The Semantic Progress Function represents cumulative semantic state as a one-dimensional curve whose slope measures instantaneous semantic change.
- Semantic linearization reparameterizes sequences so semantic progress increases at a constant rate, producing smoother and more predictable transformations without model fine-tuning.
- The framework analyzes synthetic and real-world sequences and applies SPF-derived retiming strategies to correct uneven evolution and enforce desired pacing.
2 Related Work
Prior work seeks coherent transitions through morphing, latent interpolation, diffusion, and temporal control, while this framework focuses on measuring and correcting semantic pacing without manual annotation.
- Existing transition methods span geometric morphing, latent-space interpolation, and diffusion-based synthesis, generally targeting coherent intermediate visual content.
- Video generative models add realistic motion and dynamics but face temporal consistency challenges and use first-last-frame conditioning to bridge endpoints.
- TempoControl manipulates cross-attention to align video frames with prompt segments but requires manually specified spatial masks.
- The proposed approach converts generated and in-the-wild videos to constant semantic pace without manual annotation and introduces objective temporal-linearity measurement.
3 Semantic Progress Function
The Semantic Progress Function converts framewise semantic distances into a one-dimensional cumulative trajectory whose slope exposes pacing, then uses weighted fitting and retiming to reveal and correct irregularities.
- The SPF is a model-agnostic scalar trajectory whose value represents cumulative semantic state and whose slope reflects instantaneous semantic change.
- The construction computes pairwise semantic distances between frames and integrates them over time so SPF differences approximate those distances.
- Frames are embedded with SigLIP, and semantic distances are computed using an angular metric after ℓ2 normalization.
- In practice, pairwise constraints may be restricted to frames within 30 temporal positions to improve efficiency and emphasize local structure.
- The SPF is estimated with regularized weighted least squares, using weights that favor temporally local constraints through a Gaussian temporal-distance function.
- For λ > 0, the fitting objective is strictly convex and has a unique closed-form solution.
- In the cat-to-lion example, the raw SPF sharply accelerates at the semantic discontinuity, whereas retiming produces steadier semantic progression.
4 Video Linearization via ReTime
Semantic linearization retimes generated or existing videos so semantic change follows a more uniform pace. The approach uses SPF-based temporal warping, frequency-aware modulation, iterative refinement, and segment-based regeneration when direct model control is unavailable.
- Semantic linearization reparameterizes time so semantic change progresses at a constant rate, without retraining or manual annotation.The correction redistributes temporal capacity according to measured semantic change.
- Temporal Position Warping: SPF inversion maps desired uniform progress to warped temporal positions, stretching rapid-change regions and compressing stable ones.Piecewise-linear interpolation is used over discrete SPF samples.
- Frequency-Aware Warping: Frequency-aware RoPE warping applies stronger correction to low-frequency bands while limiting high-frequency warping that can induce local jitter.The band strength decays exponentially from low to high frequencies.
- Timestep-Dependent Modulation: An exponential denoising schedule applies stronger warping early, concentrating semantic correction during structure formation while allowing later detail refinement.The effective per-band strength is the band strength multiplied by a timestep-dependent decay factor.
- Iterative Refinement: Three refinement iterations are empirically sufficient to achieve near-linear semantic progression.The iterative scheme updates warped positions using the current video’s SPF and band-specific step sizes.
- Retiming Existing Videos: For uncontrolled videos, segmented least squares partitions the SPF into contiguous regions whose keyframes guide intermediate clip regeneration.Segment durations are allocated in proportion to cumulative semantic change, supporting open- or closed-source models conditioned on keyframes or first-last frames.
5 Experiments
Experiments evaluate SPF analysis and retiming across generated and real videos, controlled synthetic motion, embedding choices, and quality preservation. Results show that SPF-guided methods correct uneven semantic pacing while preserving visual fidelity and supporting non-linear target pacing.
- Experimental Scope: The evaluation suite compares baseline retiming strategies, tests real cinematic footage, validates SPF against controlled pacing profiles, and analyzes hyperparameter sensitivity.Quantitative metrics and a user study assess visual-fidelity preservation and semantic-pace regularization.
- Retiming Comparisons: Our feature-space retiming avoids the ghosting produced by pixelwise interpolation and the external model’s quality bottleneck in a strawberry→bird transition.The method operates directly on underlying model features, preserving the input model’s intrinsic capacity.
- Real Cinematic Video: Semantic linearization redistributes an abrupt lighting-cued metamorphosis into a smooth evolution from human to monster in real cinematic footage.The regenerated sequence captures gradual background growth and a steady transformation.
- Non-Linear Retiming: The framework also retimes videos to rising and falling exponential pacing functions, producing acceleration and deceleration around the sun’s entry.This demonstrates control beyond constant semantic speed.
- Synthetic Validation: Synthetic rotating-spot videos with constant, rising-exponential, and falling-exponential velocities test whether SPF recovers the imposed semantic pace.The minimalist scene isolates rotation pace as the main source of variation.
- SPF Hyperparameter Ablation: SigLIP provides the best fine-grained semantic sensitivity, while pixelwise L2 fails to capture the rapid man-to-gorilla transition.SigLIP detects a local peak at the onset of the subject’s anger and is adopted as the default embedder.
- SPF Hyperparameter Ablation: p=2 yields superior segmentation results for existing-video regeneration, although p=1 is the typical default.Increasing p increases the semantic curve’s contrast.
- Quantitative Evaluation: Retimed videos remain within one standard deviation of baseline VBench quality across metrics, while 88% of users prefer their semantic pacing.The evaluation covers N=128 retimed videos per model, and Table 1 reports equivalent quality to the original input across metrics.
6 Conclusions, Limitations, and Future Work
The paper concludes that SPF provides an interpretable, model-agnostic way to measure semantic pacing and identify abrupt transitions, while semantic linearization enables smoother, more predictable transformations without retraining. Its analysis remains sensitive to non-semantic appearance changes and excessive iterative refinement, motivating motion-aware and multi-dimensional extensions.
- Conclusions: SPF reduces complex transformations to a one-dimensional semantic trajectory for measuring pacing, identifying abrupt transitions, and comparing generative processes.The representation is presented as simple, interpretable, and model-agnostic.
- Conclusions: Semantic linearization reparameterizes time for constant semantic speed through generation-time positional-embedding warping or post-hoc segmented regeneration.Both realizations enable smoother and more predictable transformations without model retraining or manual annotation.
- Limitations: Frame-level embeddings may reflect perceptual rather than purely semantic change under rapid camera motion, strong lighting changes, or large non-semantic appearance variations.Fully disentangling motion, appearance, and semantics remains an open challenge.
- Future Work: Future work proposes motion-aware or temporally grounded embeddings and joint analysis of semantic dimensions such as identity, style, and geometry.These directions target robustness in dynamic scenes and richer control over different aspects of change.