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Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them

Woojung Han, Seil Kang, Youngjun Jun, Min-Hung Chen, Fu-En Yang, Seong Jae Hwang

arXiv:2606.06361v2cs.CV

TL;DR

Image-to-video diffusion models often generate physically implausible motion despite visual physical cues. PhaseLock preserves motion priors from 2-step inference during high-fidelity denoising, improving physical consistency by 6.2 points on average at negligible cost.

  • Problem

    Image-to-video models still produce physically implausible dynamics despite visual physical cues, while existing remedies can require substantial computation or annotation.

  • Method

    PhaseLock is a training-free strategy that extracts motion priors from 2-step inference and preserves them during high-fidelity generation through Latent Delta Guidance.

  • Results

    6.2 points average improvement in physical consistency, with 1.06× time and 1.02× memory while largely maintaining visual fidelity.

  • Takeaways & Limitations

    Physically consistent motion can be established within a few denoising steps and preserved rather than recovered through additional computation or external guidance.

  • Takeaways & Limitations

    PhaseLock transfers whatever motion is captured by its 2-step pass, so its benefit depends on the quality of that motion prior.

Abstract

from arXiv · show

Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising finding: a 2-step generation often exhibits better physical consistency than a 50-step output from the same model. Through spectral analysis, we trace this to phase erosion during denoising; the phase degrades significantly (dropping by $\approx 18\%$ from step 2 to step 50), whereas the magnitude remains relatively stable. Building on this insight, we propose PhaseLock, a training-free framework that preserves the valid motion priors from few-step inference throughout the denoising trajectory. Rather than relying on full-step inference for physical consistency, PhaseLock extracts a motion prior from just 2 steps and enforces it onto high-fidelity generation via Latent Delta Guidance. Our approach effectively mitigates phase degradation, improving physical consistency by an average of 6.2 points across diverse models while largely maintaining visual fidelity, with negligible overhead ($1.06\times$ time, $1.02\times$ memory) and reduced reliance on expensive external guidance methods ($\sim5\times$ time). Project Page: https://dnwjddl.github.io/phaselock

1. Introduction

Image-to-Video diffusion models render scenes faithfully but often generate motion that violates physical laws. PhaseLock addresses this by extracting physical motion priors from 2-step inference and preserving them during high-fidelity denoising, improving physical consistency with minimal overhead.

  • Motivation: Image-to-Video generation fixes the initial scene through an input image, making subsequent motion the primary degree of freedom for studying physical failure.Input images can provide grounded cues about material properties, structural constraints, and plausible physical states when text descriptions are ambiguous.
  • Observation: 2-step inference often produces better physical structure than 50-step inference, which provides greater visual detail but can compromise physical integrity.The finding motivates preserving early denoising structure instead of assuming longer inference necessarily improves physical consistency.
  • Frequency-domain analysis: 18%: phase drops from step 2 to step 50 while the magnitude spectrum remains relatively stable, indicating that denoising mainly disrupts structural dynamics.Magnitude captures appearance-related energy, whereas phase determines the spatial organization of structures across frames.
  • Method: PhaseLock is a model-agnostic, training-free strategy that uses NFE = 2 physical priors and Latent Delta Guidance to preserve the structural phase spectrum.It computes inter-frame deltas from the few-step latent and applies them during denoising.
  • Results: 6.2 points: PhaseLock improves physical consistency on average across diverse models, with 1.06× time and 1.02× memory overhead.The method largely maintains visual fidelity while requiring much lower computational cost than comparable external guidance.

2. Related Works

Prior work has established that video diffusion models achieve high visual quality but often violate physical consistency, motivating dedicated benchmarks and analyses. Related training-free methods and frequency-based views improve inference or explain diffusion dynamics, but the supplied passages position these as broader foundations for this work.

  • Physical Consistency in Video Generation: Video diffusion models achieve remarkable visual quality yet consistently struggle with physical consistency.Benchmarks including VideoPhy and PhyGenBench evaluate physical plausibility using VLM-based protocols.
  • Physical Consistency in Video Generation: VideoPhy and PhyGenBench assess physical plausibility through VLM-based evaluation protocols.The passage identifies these benchmarks as tools for evaluating physical consistency in generated videos.
  • Physical Consistency in Video Generation: At T = 2, baseline videos accurately follow the physical trajectory, whereas T = 50 can exhibit severe physical hallucination and move in the opposite direction.Figure 2 compares CogVideoX and Wan 2.1 at few-step and default-step inference against the ground-truth video.
  • Training-Free Inference Strategies: Training-free inference methods improve diffusion outputs through guidance, attention manipulation, and semantic interventions without retraining.Video-specific examples include FreeInit, which refines low-frequency initialization components, and TokenFlow, which propagates consistent features across frames.
  • Frequency Analysis and Diffusion Dynamics: Diffusion dynamics are commonly characterized as coarse-to-fine generation, with global structures forming early and high-frequency details emerging later.The passage cites EDM and Cold Diffusion as prior diffusion-dynamics studies and describes spectral autoregression as formalizing this frequency-based view.

3. Mechanism Analysis

Mechanism analysis identifies a trade-off between visual refinement and dynamic consistency: few-step inference can preserve physically valid motion that weakens during later refinement. Frequency-domain evidence attributes this degradation primarily to phase erosion, while magnitude remains comparatively stable.

  • Phase Degradation: Phase degradation is identified as the key mechanism behind the trade-off between visual refinement and dynamic consistency.The phase spectrum encodes spatio-temporal structure and motion trajectories, whereas the magnitude spectrum captures low-level texture and contrast statistics.
  • Few-Step versus Full Denoising: Few-step inference can produce more physically consistent motion than 50-step denoising, despite lower visual fidelity.Extending denoising from 50 to 100 steps improves physical consistency by only about 1 point while substantially increasing inference time.
  • Phase Sensitivity of Motion Dynamics: Physical dynamics degrade rapidly under phase corruption while remaining comparatively stable under magnitude corruption.Controlled spectrum-corruption experiments and phase-sensitivity analysis show that motion is highly sensitive to phase changes.
  • Low-Frequency Structural Integrity: 3.6× higher phase-difference correlation is retained by the 2-step output than the 50-step output under strong blur, with correlations of 0.358 and 0.100.This supports genuine structural phase alignment rather than a blurriness artifact.
  • Why Few Steps Preserve Physics: 8.5× EPE disparity supports the explanation that low-NFE inference preserves physics by limiting exposure to refinement, where phase is more vulnerable than magnitude.The passage reports magnitude changes of only ∼2–3%, while inter-frame phase differences closely reflect motion.

4. PhaseLock

PhaseLock is a training-free, spatial-domain framework that extracts motion priors from few-step inference and preserves them during high-fidelity denoising. It uses latent inter-frame differences to guide temporal evolution while decaying guidance as detail refinement begins.

  • Framework: PhaseLock avoids explicit spectral substitution or low-frequency injection, instead using a spatial-domain strategy to preserve phase-informed motion priors without high-frequency artifacts or spatial incoherence.The method is training-free and guides high-fidelity generation with phase-preserving motion priors.
  • Motion Prior Extraction: The Latent Delta Operator computes inter-frame latent differences, capturing local temporal dynamics while suppressing time-independent features such as static backgrounds.T(z) = z2:F − z1:F−1.
  • Two-Stage Generation: PhaseLock first generates a coarse motion prior using Kfew denoising steps, such as Kfew = 2, before applying it during Kfull-step high-fidelity generation, such as Kfull = 50.The two stages reuse the same initial Gaussian noise, aligning the high-fidelity trajectory with the few-step prior.
  • Latent Delta Guidance: At each full-generation step, guidance equals the residual between the target motion prior and current latent dynamics, modifies subsequent frames, and leaves the first frame as an anchor.The residual is G(k) = Mprior − T(z(k)); λ(k) controls guidance strength.
  • Adaptive Scheduling: Guidance is active only over an early interval and decays linearly, enforcing motion consistency during global layout formation while relaxing constraints for texture refinement.The schedule uses the interval [kstart, kend) and reduces interference with later detail rendering.
  • Theoretical Basis: Under smooth-motion assumptions, latent deltas approximate inter-frame phase differences, so minimizing motion-prior residuals implicitly constrains phase evolution without explicit spectral manipulation.The approximation assumes similar consecutive-frame magnitudes and small phase shifts, |ϕf − ϕf−1| ≪ 1.

5. Experiments

PhaseLock is evaluated across multiple video-generation backbones using Physics-IQ, PhyGenBench, VBench, human preferences, qualitative comparisons, and efficiency analyses. It improves physical consistency while preserving visual quality, maintaining low overhead, and achieving best performance with two few-step inferences.

  • Experimental Setup: Experiments span CogVideoX-5B, Wan 2.1, and LTX-Video to test PhaseLock across diverse video-generation architectures.The method is applied without fine-tuning pretrained weights, using a training-free adaptation.
  • Quantitative Evaluation: Physics-IQ evaluates objective motion correctness across solid mechanics, fluid dynamics, optics, and magnetism, while PhyGenBench assesses holistic physical plausibility via an LVLM.The I2V adaptation of PhyGenBench uses input images generated by Gemini-2.5-flash and FLUX-schnell.
  • Quantitative Evaluation: PhaseLock largely preserves baseline VBench performance while improving dimensions including Background Consistency and Image Quality.Wan 2.1 exhibits a modest drop in Aesthetic Quality.
  • Human Study: Human evaluations show significant improvements across Physics Plausibility, Visual Quality, and Prompt Alignment.The study uses 15 annotators and aggregates pairwise judgments with win rates and accuracy scores.
  • Efficiency Analysis: 36.0 vs. 36.3 on Physics-IQ is achieved by PhaseLock versus WMReward, while PhaseLock incurs only a 1.06× inference-time overhead.PhaseLock avoids external reward models, gradient backpropagation, and expensive Best-of-N search through lightweight latent operations.
  • Ablation Study: NFE = 2 and motion strength 0.05 maximize Physics-IQ performance, while higher motion strengths reduce performance.The ablation varies motion strength from 0.03 to 0.13 and NFE from 2 to 10.

6. Discussion

PhaseLock is most beneficial when coherent inter-frame motion is critical, including diverse physical phenomena and sharp-boundary or high-frequency events. Its limitations and future directions concern dependence on the captured 2-step motion prior, phase-aware training, combined physics methods, and adaptive guidance.

  • Benefits: PhaseLock benefits scenarios requiring coherent inter-frame motion across liquids, thermodynamics, magnetism, deformable objects, and rigid-body motion.Latent Delta Guidance constrains the inter-frame velocity field in the spatial domain.
  • Benefits: PhaseLock can also benefit sharp-boundary and high-frequency events through spatial-domain constraints on inter-frame velocity.The passage attributes this benefit to Latent Delta Guidance.
  • Limitations: PhaseLock transfers whatever motion is captured by the 2-step pass rather than injecting additional motion.The supplied limitation passage ends after this statement, so no further limitation is specified.
  • Future Directions: Phase-aware training losses are proposed as a complementary training-time remedy because phase erosion may arise from magnitude-weighted common MSE-style objectives.The discussion also suggests combining PhaseLock with training-based physics methods and adapting guidance strength to few-step-prior reliability.

7. Conclusion

The paper concludes that video diffusion models form physically consistent motion in just 2 steps but overwrite it during refinement, motivating PhaseLock’s preservation strategy. PhaseLock improves physical consistency by 6.2 points on average at negligible cost without external guidance or additional training.

  • Conclusion: 2 steps establish physically consistent motion priors that visual refinement progressively overwrites.The conclusion frames this as evidence that preserving valid motion knowledge can matter more than adding computation.
  • Conclusion: PhaseLock locks motion priors before they are lost, improving physical consistency by 6.2 points on average.The method operates without external guidance or additional training.
  • Conclusion: PhaseLock achieves these gains at negligible cost, requiring 1.06× time.The conclusion presents smarter preservation as a direction for physics-aware generation.

Impact Statement

The work investigates diffusion models’ internal mechanisms to mitigate physical hallucinations in video generation. It targets reliable applications while acknowledging societal risks from increasingly capable generative systems.

  • Research impact: The research investigates diffusion models’ internal mechanisms to mitigate physical hallucinations in video generation.It aims to improve adherence to physical laws.
  • Research impact: Ensuring adherence to physical laws could support high-reliability applications such as robotics simulation and scientific visualization.
  • Research impact: The authors acknowledge societal risks from advancing generative capabilities, including the creation of misleading media.

Technical Appendices … D.3.2. EXPERIMENTAL SETUP

The appendices detail PhaseLock’s experimental foundations, causal analyses, theoretical justification, algorithm, and comparisons showing that spatial-domain guidance is preferable to direct frequency manipulation. Across these analyses, phase is linked to motion dynamics, while explicit spectral surgery destabilizes latent representations.

  • B.1. Setup Details; B.2. Causal Studies: The setup uses Physics-IQ GT videos with CogVideoX and Wan 2.1, analyzing 30–50 paired samples mechanistically and all 396 videos for benchmark results.The analyses rely on the denoising process and compare outputs against ground-truth videos.
  • B.2. Causal Studies: 5.39× the magnitude-corruption error is reached by phase corruption at α = 0.75 for jerk, while magnitude corruption leaves smoothness nearly unchanged.The causal study evaluates trajectory error, velocity distortion, and motion smoothness using hierarchical motion metrics.
  • B.2. Causal Studies: 81px at α = 0.25 marks magnitude-corruption trajectory error saturation, whereas phase corruption progressively increases positional error and surpasses magnitude error at α ≥0.5.The result supports phase as a strong contributor to structural object location.
  • B.2. Causal Studies: 2.32× higher error occurs under phase than magnitude corruption at α = 0.5, while magnitude corruption has negligible, intensity-independent impact on velocity profiles.Phase-induced positional jitter is amplified in velocity, disrupting consistency associated with physical motion.
  • B.3. Why Low-NFE Exhibits Better Physics: Early denoising resolves global low-frequency structure, so 2-step outputs already capture predominantly low-frequency physical motion before later high-frequency detail refinement.The low-NFE advantage is presented as structural: fewer steps limit exposure to phase-vulnerable denoising.
  • C. Proof of Latent Delta Encoding Phase Differences; C.1. Setup and Notation; C.2. Derivation; C.3. Interpretation; C.4. General Case: Unequal Magnitudes: Under L2-style objectives, phase correction is structurally weaker where magnitude is small, and latent-delta magnitude encodes inter-frame phase differences that govern motion dynamics.The spatial L2 constraint aggregates phase alignment across frequencies through Parseval’s theorem, including when consecutive-frame magnitudes differ.
  • D. Methods Details; D.1. Algorithm of PhaseLock; D.2. Impact of Inference Steps on Physical Consistency: PhaseLock extracts a coarse motion prior with Kfew = 2 and guides Kfull = 50 generation using a linearly decaying strength λ applied to latent temporal deltas.The procedure reuses the initial noise, computes the current motion delta, and applies the prior difference during guided denoising.
  • D.3. Why Direct Frequency Manipulation Fails; D.3.1. BASELINE METHODS; D.3.2. EXPERIMENTAL SETUP: All direct frequency manipulation methods substantially underperform the unmodified baseline, suggesting that explicit spectral surgery destabilizes latent representations rather than preserving physical consistency.The CogVideoX-5B experiments apply guidance at each k ∈[0, K/2) to match PhaseLock’s schedule and compare four frequency-manipulation strategies.

D.3.3. ANALYSIS: WHY DIRECT FREQUENCY MANIPULATION FAILS · D.3.4. WHY LATENT DELTA GUIDANCE SUCCEEDS · D.4. Spectral Analysis of PhaseLock

Direct frequency manipulation fails because spectral surgery introduces artifacts, disrupts coupled latent structure, and cannot enforce physics across frequency bands. Latent Delta Guidance instead constrains spatial inter-frame changes, preserving learned denoising dynamics while PhaseLock evaluates phase coherence and magnitude correlation against ground truth.

  • D.3.3. ANALYSIS: WHY DIRECT FREQUENCY MANIPULATION FAILS: Direct frequency manipulation fails for three reasons: FFT artifacts, latent-space structure mismatch, and frequency-domain constraints that do not preserve physical properties.The analysis identifies these as fundamental failure modes.
  • D.3.3. ANALYSIS: WHY DIRECT FREQUENCY MANIPULATION FAILS: 8.5× greater motion sensitivity to phase corruption than magnitude corruption shows that surgically replacing phase can disrupt latent coherence.Phase and magnitude are hypothesized to encode semantic content jointly, so phase substitution may produce representations outside the learned latent manifold.
  • D.3.3. ANALYSIS: WHY DIRECT FREQUENCY MANIPULATION FAILS: Physical consistency spans all frequencies through conservation laws, collision dynamics, and temporal causal ordering, so low-frequency manipulation alone is insufficient.Temporal smoothness can coexist with physically invalid motion, while sharp velocity reversals can occur in physically correct motion.
  • D.3.4. WHY LATENT DELTA GUIDANCE SUCCEEDS: Latent Delta Guidance constrains inter-frame changes in the spatial domain rather than directly manipulating spectral components.This avoids FFT/IFFT operations and eliminates boundary spectral leakage and frequency-filtering ringing artifacts.
  • D.3.4. WHY LATENT DELTA GUIDANCE SUCCEEDS: Parseval’s theorem makes spatial-domain delta minimization equivalent to an aggregate constraint on squared spectral differences, emphasizing phase evolution without per-frequency intervention.Under typical video statistics, latent delta magnitude is dominated by inter-frame phase differences.
  • D.3.4. WHY LATENT DELTA GUIDANCE SUCCEEDS: Magnitude-weighted guidance prioritizes high-magnitude components carrying perceptually significant motion while allowing flexibility for low-magnitude noise and texture.The guidance weights components by squared magnitude A[ω]2.
  • D.3.4. WHY LATENT DELTA GUIDANCE SUCCEEDS: The soft guidance signal G(k) = Mprior −M(k) nudges denoising toward the motion prior while preserving the diffusion model’s learned-manifold dynamics.The key distinction is constraining changes between frames rather than absolute spectral values; Table 7 empirically validates this aggregate approach.
  • D.4. Spectral Analysis of PhaseLock: PhaseLock’s spectral analysis measures low-frequency phase coherence and log-magnitude correlation against ground-truth 16-frame videos using 3D FFT components within 40% of Nyquist.Phase coherence is computed as E[cos(ϕreal −ϕgen)], while magnitude correlation uses Pearson’s r.

E. Evaluation Details · F. Additional Experiment Results · F.1. Additional Ablation Studies

The evaluation combines physics-focused benchmarks, general video-quality assessment, and blinded human preference studies. Additional ablations examine adaptive scheduling, guidance formulations, and the timestep range for Latent Delta guidance.

  • E. Evaluation Details: Physics-IQ evaluates physical understanding using 396 real-world videos spanning 66 physical scenarios.The scenarios cover principles including solid mechanics, fluid dynamics, optics, and magnetism.
  • E. Evaluation Details: PhyGenBench evaluates physical commonsense with 160 prompts probing 27 physical laws across mechanics, optics, thermal dynamics, and material properties.Its PhyGenEval framework uses Vision-Language Models for hierarchical assessment.
  • E. Evaluation Details: PhyGenBench is adapted to image-to-video by generating initial-condition images with Gemini-2.5-flash-image-preview and FLUX-schnell.The original prompts were modified to describe the scene immediately preceding the video.
  • E. Evaluation Details: VBench assesses general video quality alongside Physics-IQ and PhyGenBench, covering image quality, aesthetic quality, and related generation dimensions.This evaluation checks whether physical alignment preserves overall visual fidelity and temporal coherence.
  • E. Evaluation Details: Human evaluation uses randomized, blind pairwise comparisons of Ours versus Baseline across physics plausibility, prompt alignment, and visual quality.Results report Win Rate and Accuracy, with ties contributing 0.5 to Accuracy.
  • F. Additional Experiment Results: Additional experiment results extend the evaluation beyond the main paper’s space constraints.The supplementary studies focus on ablations of scheduling, guidance formulation, and timestep selection.
  • F.1. Additional Ablation Studies: Adaptive scheduling is compared with alternative scheduling strategies to assess its effectiveness on CogVideoX-5B.The experiments are conducted using the Physics-IQ benchmark.
  • F.1. Additional Ablation Studies: Further ablations analyze guidance formulations and the timestep range used for Latent Delta guidance.These studies validate the choice of Latent Delta guidance and investigate how its application range affects results.

F.1.1. SCHEDULING ABLATIONS … G. Limitations and Further Discussions

PhaseLock performs best with gradual early-to-late guidance decay and first-order latent-difference transfer, while sensitivity analyses show that fewer inference steps and guidance through roughly the first half of denoising preserve motion most effectively. Across architectures and scenarios, gains are consistent but heterogeneous, especially for non-rigid dynamics, with additional qualitative results reported separately.

  • F.1.1. SCHEDULING ABLATIONS: 36.0: Linear decay outperforms alternative schedules by anchoring coarse motion early while releasing constraints for late visual refinement.Constant scheduling scores 31.32, exponential decay 34.91, and cosine scheduling 32.92.
  • F.1.2. GUIDANCE FORMULATION ABLATION: 15.97: Direct Latent guidance performs poorly because it conflates static content with motion dynamics and interferes with legitimate visual refinement.Second Order guidance performs worst at 11.89, while normalized guidance scores 31.34.
  • F.1.2. GUIDANCE FORMULATION ABLATION: First-order, unnormalized frame differences provide the effective guidance signal because they encode motion phase while remaining invariant to static scene content.This formulation targets the velocity field without disrupting appearance refinement.
  • F.1.3. HYPERPARAMETER SENSITIVITY.: 36.0: Performance peaks at guidance strength λ0 = 0.05, forming an inverted-U pattern as weak guidance permits phase drift and strong guidance over-constrains refinement.Weak guidance at λ0 = 0.03 scores 34.1; guidance at λ0 ≥0.10 becomes over-constraining.
  • F.1.3. HYPERPARAMETER SENSITIVITY.: 36.0: On CogVideoX, Physics-IQ decreases from 36.0 at NFE=2 to 32.8 at NFE=5 and 30.5 at NFE=10, consistent with accumulating phase erosion.Wan 2.1 shows the same monotonic decrease, from 28.7 at NFE=2 to 24.2 at NFE=10.
  • F.2. Architectural Generality: +8.9: PhaseLock improves Physics-IQ across SVD, SkyReels-V2, and Wan 2.1 Distilled, spanning UNet-based, alternative-scheduler, and Lightweight DiT architectures.The smaller gain on SVD is +1.2, while improvements remain consistent across architectural families.
  • F.3. Per-Scenario Performance Characterization: +55.4: Representative gains concentrate in complex fluid and deformable dynamics, while sharp-transition cases also benefit because early steps establish velocity and later steps render detail.Examples include napkinsoak +54.7, cut-orange +51.9, silk-cover +35.8, and potato-in-water +18.3; additional qualitative Physics-IQ and PhyGenBench results are reported in figures.

G.1. Limitations and Future Works … G.2.6. SOCIETAL CONSIDERATIONS

The paper identifies limitations in PhaseLock’s dependence on few-step motion quality, diffusion architectures, and user control, then outlines theoretical, sampling, modality, practical, and societal extensions. It also highlights both beneficial applications and misuse risks of more physically realistic synthetic video.

  • G.1. Limitations and Future Works: PhaseLock can inherit erroneous motion from incorrect 2-step inference, does not directly apply to autoregressive models, and may constrain intentionally unrealistic motion.Guidance can follow implausible few-step trajectories; autoregressive models lack a denoising loop, while adjusting λ0 or disabling guidance restores creative flexibility.
  • G.2. Further Discussions: Table 18 compares physics-guided video generation methods across performance, cost, and applicability.
  • G.2.1. WHY DOES PHASE EROSION OCCUR?: Phase erosion may arise because MSE and perceptual objectives under-weight phase errors in fine-detail regions, while coarse-to-fine generation establishes phase early and later refinement can overwrite it.Future work proposes rigorous analysis under MSE, perceptual, and adversarial losses to inform physics-aware training.
  • G.2.2. TOWARD PHYSICS-AWARE SAMPLERS: Physics-aware samplers could preserve phase by allocating capacity to early denoising, reducing mid-to-late steps, and adapting noise across frequency bands.Frequency-adaptive sampling may improve physics without external guidance but requires per-step latent frequency decomposition and adds computational overhead.
  • G.2.3. EXTENSION TO OTHER MODALITIES: The phase-erosion phenomenon may extend to audio, 3D, and multimodal generation through temporal, geometric, or cross-modal structural inconsistencies.Proposed extensions include transferring few-step rhythm and pitch priors to audio and extracting structural priors for 3D generation.
  • G.2.4. THEORETICAL EXTENSIONS: Future theoretical work could explain phase vulnerability through information theory, optimal-transport geometry, and the distinction between structural memorization and appearance generalization.The supplied discussion links phase erosion to information loss and possible structural forgetting during generation.
  • G.2.5. PRACTICAL EXTENSIONS: Practical extensions include user-specified or interpolated motion priors, chunk-consistent long-video guidance, and further optimization for real-time generation.Real-time optimization ideas include caching the 2-step prior, amortizing extraction, and distilling guidance into model weights.
  • G.2.6. SOCIETAL CONSIDERATIONS: Physically consistent video can support robotics, scientific visualization, autonomous-vehicle testing, and accessibility, but may also enable convincing deepfakes, fabricated evidence, and misinformation.Suggested mitigations include artifact detection, latent-level watermarking, and responsible disclosure; phase analysis may aid synthetic-content detection.
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