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REVIEW 4 major objections 4 minor 50 references

Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation

T0 review · 4 major / 4 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read Wavelet-domain phase injection with low-frequency randomization achieves photorealistic, semantically consistent sim-to-real translation without paired data or conditioning, and measurably improves a downstream vision-language planner.

desk verdict Solid engineering extension of NeuralRemaster with a genuine new capability, but the phase mechanism is never isolated; treat the strong claims as conditional. read the letter →

arxiv 2607.21628 v2 pith:3WMXPSFT submitted 2026-07-08 cs.AI eess.IV

Kaiwen Wang , Frank Bieder , Yinzhe Shen , Carlos Fernandez , Jan-Hendrik Pauls , Omer Sahin Tas This is my paper
classification cs.AIeess.IV
keywords sim-to-realtranslationwaveletphasediffusiondual-treecomplexpackettransformpreservationlow-frequencyrandomizationmodelssemanticconsistencyautonomousdriving
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper's central claim is that the phase-magnitude decomposition known from signal processing also works when applied to the latent space of a diffusion model, provided the transform is spatially localized. By replacing Fourier phase preservation with a dual-tree complex wavelet packet transform, the method avoids the global ringing and boundary leakage that made Fourier phase injection hallucinate false geometry. A second component randomizes the low-frequency wavelet packet, letting the model ignore synthetic illumination and generate in-distribution real appearance. On vKITTI→KITTI, the method is the only one to lead simultaneously in realism (KID 4.41), perceptual quality (CLIP-IQA 0.561), and semantic consistency (mIoU 43.50) among translation methods; on CARLA video it reduces a vision-language planner's average trajectory error by 5.4% and final error by 5.1%. The same spatially local cutoff map enables zero-shot instance-level translation, where only a selected object becomes photorealistic while the rest of the frame remains untouched.

What carries the argument

The central mechanism is phase injection in the dual-tree complex wavelet packet transform (DT-CWPT) domain. The transform decomposes a latent into one real low-frequency packet and complex high-frequency packets with localized spatial support; source phase is injected packet-wise according to a cutoff radius map, so structural information is preserved locally without global spectral coupling. The second component, Low-Frequency Randomization (LFR), replaces the source low-frequency packet with the noise packet, removing the synthetic illumination prior. Together they form the structured noise that drives the diffusion backbone, and the same cutoff map enables instance-level translation. The

What would settle it

Run two matched vKITTI→KITTI translations with ψ-PD: one with genuine source phase injection and one with source and noise phases swapped. If swapped-phase outputs keep mIoU and DepSSIM at the same level, phase is not the carrier of the reported structural consistency. As a second check, profile pixel values across a high-contrast edge: Fourier phase injection should show overshoot and ringing while DT-CWPT should not.

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Extended reading notes

Core claim

The paper proposes ψ-PD, a training recipe for sim-to-real translation that needs no paired real-synthetic data and no inference-time conditioning signals. The method constructs diffusion noise by taking a Gaussian sample and, in the dual-tree complex wavelet packet (DT-CWPT) domain, injecting the phase of the source latent while randomizing the low-frequency packet. Because wavelet packets have localized support, the phase constraint acts where it is needed instead of coupling all frequency bands globally as the Fourier transform does. The paper reports that this yields a strictly better realism–consistency operating point than prior phase-preserving diffusion: best KID 4.41, best CLIP-IQA

Load-bearing premise

The load-bearing premise is that phase carries spatial structure in a VAE's latent codes and in DT-CWPT coefficients just as it does in pixel-domain images; the paper provides no isolated experiment testing that transfer, so if latent-space phase does not align with geometry the mechanism loses its grounding.

Editorial extensions

If this is right

  • Sim-to-real pipelines can gain realism and semantic consistency without paired datasets or control modules, removing a major data-engineering cost.
  • Because the overhead is under 0.15% of inference time, the method can be dropped into existing image and video diffusion backbones with negligible latency cost.
  • Downstream planners benefit directly: translated CARLA video reduces predicted-trajectory error by about 5%, suggesting closed-loop simulation evaluation becomes more reliable.
  • Instance-level translation is available zero-shot from a per-pixel cutoff map, enabling selective photorealism editing of single objects in a scene.
  • Localized phase injection avoids the geometric hallucinations (wrong vehicle orientation, dashboard artifacts) that Fourier-domain phase preservation produced in the qualitative comparisons.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural stress test is to swap the roles: inject source magnitude with noise phase. If semantic consistency survives, the mechanism is not specifically phase; if it collapses, the phase hypothesis is confirmed.
  • The same DT-CWPT phase injection could likely be ported to other generative backbones without retraining them, since the construction lives in the transform and noise schedule rather than the model weights.
  • The spatial cutoff map suggests a cheap interactive editing tool: a user-provided mask or depth map could set per-region cutoff radii, making photorealism a continuous local dial rather than a global setting.
  • Low-Frequency Randomization might be imported into Fourier-domain methods as a partial fix for global illumination bias, although the ringing problem would remain; comparing the two would isolate how much of the gain comes from locality versus randomization.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces ψ-PD, a diffusion-based sim-to-real translation method that performs phase injection in the dual-tree complex wavelet packet transform (DT-CWPT) domain rather than the Fourier domain, and replaces the low-frequency wavelet packet with the corresponding packet of random noise (Low-Frequency Randomization, LFR). The method is applied in the latent space of FLUX for images and Wan 2.2 for videos, after fine-tuning on unpaired open-domain data. Experiments on vKITTI→KITTI report state-of-the-art KID, CLIP-IQA, and mIoU among non-trivial methods, and on CARLA report that ψ-PD is the only method to improve both realism and VLM-planner ADE/FDE. The paper also demonstrates zero-shot instance-level translation via spatially varying cutoff maps.

Significance. If the reported results are robust, the contribution is useful: ψ-PD requires no paired data or inference-time conditioning, introduces negligible overhead, supports instance-level control, and is evaluated with a downstream planning metric, which is a valuable complement to standard realism metrics. The ablation design comparing DT-CWPT vs. Fourier phase injection and LFR on/off is conceptually appropriate, and the authors provide code and models. The central mechanistic claim, however, is not isolated: no experiment separates phase preservation from wavelet locality, LFR, or fine-tuning, and the empirical evidence consists of single-run point estimates without error bars.

major comments (4)
  1. [§4.2, Eq. (5); §5.6] The causal role of phase is not established by the ablations. Comparing ψ-PD w/o LFR with NeuralRemaster varies the transform (DT-CWPT vs. Fourier) and packet-wise thresholding while both methods inject source phase; comparing full ψ-PD with ψ-PD w/o LFR varies only the low-frequency packet. No arm randomizes or removes the source phase in the high-frequency packets while keeping DT-CWPT locality, LFR, and fine-tuning fixed. The reported gains could therefore be due to wavelet locality, LFR, or the training protocol rather than to phase-carrying structure. Add a no-phase control (e.g., use noise phase in all packets under the same training setup) and report realism, consistency, and planning metrics for it.
  2. [§3 and §4.2] The Oppenheim–Lim premise is imported from pixel-domain signals to VAE latents and DT-CWPT coefficients without validation. Because Eq. (5) discards the low-frequency packet, the only source-phase information that survives is in high-frequency packets; the paper supplies no evidence that latent-space high-frequency wavelet phase encodes geometric or semantic structure. Provide a diagnostic (e.g., comparing phase-only vs. magnitude-only latent reconstructions for depth/semantic metrics, or a control using noise phase) to ground the transfer.
  3. [Tables 1 and 2] All reported metrics are single-run point estimates with no standard errors, confidence intervals, or significance tests. The headline margins are small (e.g., KID 4.41 vs. 4.52; ADE reduction 5.4%), so without repeated runs or paired tests the "only method" and improvement claims are not statistically supported. Report multiple runs/seeds or, at minimum, bootstrap confidence intervals.
  4. [§5.1 and §5.6] The final inference cutoff r and depth J appear to be selected from the evaluation benchmark: the text states inference uses r = half Nyquist and J=4, while §5.6 sweeps r to construct Pareto frontiers. This is tuning on the test set unless a validation protocol is defined. Specify how r and J are chosen (held-out scenes/data) and report sensitivity of Tables 1–2 to r and J.
minor comments (4)
  1. [§4.1] The symbol L is used both for the low-frequency packet and for the number of high-frequency packets L=2^J−1. This collision makes equations such as Eq. (6) confusing; rename one of them.
  2. [§4.2, Eq. (4)] f_Nyq is not defined for the VAE latent space; specify its value/units. Also clarify whether LFR is applied always at inference or with probability p=0.8 as during training.
  3. [Figure 3] The axis label 'Downsample from to ' is incomplete; the figure and caption should be completed.
  4. [Tables 1–2] It would help to state the number of test frames/images per method and whether the same random seed is used. Also specify whether CLIP-IQA is reference-free or reference-based, since that affects interpretation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical benchmark results, not derivations from fitted quantities.

full rationale

ψ-PD's pipeline is an empirical construction, not a derivation. The paper defines structured noise in Eqs. (2)-(7) by phase injection and LFR; the reported KID/CLIP-IQA/mIoU/ADE-FDE numbers are measured outcomes, not algebraic consequences. The only external principle invoked, Oppenheim-Lim [28], is cited from classical signal processing and is not a self-citation; applying it to latent DT-CWPT coefficients is an ansatz, stated as a premise, not derived from the results. The ablation (Fig. 8) compares ψ-PD w/o LFR to NeuralRemaster to isolate wavelet-vs-Fourier basis and full ψ-PD to ψ-PD w/o LFR to isolate LFR; these are independent comparisons against external baselines. No parameter is fit to a target metric and then renamed a prediction; the cutoff radius is a user-set hyperparameter. The strongest residual concern—no control that removes/randomizes source phase while holding base, LFR, and data fixed—is an attribution/experimental-design gap, not a circularity under the rubric. Hence score 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The paper's ledger consists of tuned hyperparameters (cutoff radius, decomposition depth, LFR probability) and unproven domain assumptions about phase semantics in latent and wavelet-packet spaces. The load-bearing empirical premise is that Oppenheim–Lim phase semantics survive the VAE latent transform, which is not independently demonstrated.

free parameters (3)
  • Inference cutoff radius r = r/fNyq = 0.5
    Global scalar cutoff in radial frequency coordinates controlling how much source phase is injected; chosen by hand at half the Nyquist frequency, with the operating point selected via the Pareto sweep in Fig. 8.
  • Decomposition depth J = 4 at inference
    Depth of the DT-CWPT decomposition. Trained with J sampled in [max(J_auto,3), J_auto+4] with probability 0.8; fixed to 4 at inference. Chosen by hand; ablations deferred to appendix.
  • LFR probability p = 0.8
    Probability of applying Low-Frequency Randomization during training. Set by hand without reported sensitivity analysis.
assumptions (4)
  • standard math Phase predominantly encodes spatial structure, while magnitude governs texture statistics (Oppenheim & Lim).
    Invoked in Section 3 to justify phase injection. Established for pixel-domain signals, but not automatically true for latent codes or wavelet packet coefficients.
  • domain assumption The phase-magnitude decomposition property transfers to VAE latent codes and to DT-CWPT coefficients of latents.
    The method applies Eq. (1)/(2) to latent x of FLUX/Wan and operates in the DT-CWPT domain; no experiment is provided to verify that latent-space phase preserves geometry in the same way as image-space phase.
  • ad hoc to paper Replacing the low-frequency wavelet packet with a noise packet removes synthetic illumination bias without disturbing structure.
    Core design assumption of Low-Frequency Randomization (Eq. 5). Supported only by the ablation against ψ-PD w/o LFR, not by a derivation or independent test.
  • domain assumption The diffusion prior hallucinates realistic but incorrect geometry when given spurious Fourier-domain artifacts.
    Used in Sections 5.4 and 5.6 to explain NeuralRemaster's failures and motivate wavelet locality; it is a plausible mechanism but not directly measured.

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Cite this review

Pith. "Pith review of Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation." pith.science (2026). https://pith.science/paper/3WMXPSFT

@misc{pith2026260721628,
  author       = {Pith},
  title        = {Pith review of: Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3WMXPSFT}},
  note         = {Machine review of arXiv:2607.21628}
}
abstract

Simulation-to-reality translation must bridge the appearance gap between synthetic and real domains while preserving structural and semantic consistency. Conditioning-based methods achieve spatial alignment but introduce computationally expensive control modules. Paired-data methods achieve realism but rely on complex synthesis pipelines, often altering scene geometry and semantics. Training-free editing methods avoid both constraints but lack a learned appearance prior, limiting their perceptual quality. Recently proposed phase-preserving diffusion presents a promising alternative, but Fourier-domain formulations are constrained by global spectral coupling. This coupling induces spatial artifacts such as ringing and boundary leakage, thereby degrading structural and semantic consistency. We introduce Wavelet Phase Diffusion, which addresses this through two components. First, we operate in the Dual-Tree Complex Wavelet Packet Transform domain, whose localized wavelet packets enable spatially adaptive phase injection without global spectral interference. Second, Low-Frequency Randomization (LFR) replaces the low-frequency packet, decoupling the model from the synthetic illumination prior and enabling in-distribution real-world appearance. Both components train on unpaired open-domain data, and introduce negligible inference overhead. The spatial locality further enables instance-level translation, where individual objects or regions are translated to photorealistic appearance independently while the surrounding scene remains untranslated. On vKITTI $\to$ KITTI image translation, ours outperforms prior methods in realism and semantic consistency while maintaining competitive structural alignment. For CARLA video translation, ours approaches the realism of paired-data methods while reducing VLM planner ADE and FDE by $5.4\%$ and $5.1\%$, respectively.

Figures

Figures reproduced from arXiv: 2607.21628 by the authors.

Figure 1
Figure 1. ψ-PD achieves structurally and semantically consistent sim-to-real translation without pairing data or heavy inference conditioning, and generalizes zero-shot beyond the simulation domain. Top: Global image and video sim-to-real translation. Bottom: Zero-shot instance-level video translation on a comic scene. Abstract Simulation-to-reality translation must bridge the appear￾ance gap between synthetic and real domain… view at source ↗
Figure 2
Figure 2. Fourier- vs. Wavelet-domain low-pass filtering. Fourier exhibits non-local ringing near edges, while DT-CWPT preserves local geometry and edge structure more faithfully [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of ψ-PD. Source and noise latents are decomposed by DT-CWPT into a low-frequency packet and multiple high￾frequency packets. Source phase (green) is injected per packet under the cutoff map F while the low-frequency packet is randomized (LFR), and the inverse transform yields the structured noise ˆϵ. To overcome these limitations, we instantiate T as DT￾CWPT [3] with decomposition depth J to decompose the l… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison on vKITTI → KITTI. Competing methods either hallucinate structure or distort lane/vehicle seman￾tics; ψ-PD preserves both while achieving realistic appearance. See Sec. 5.4 for per-method analysis. tively). While NeuralRemaster and Ditto translat…
Figure 5
Figure 5. Figure 5: Qualitative comparison on CARLA video translation. Baselines either retain CARLA’s synthetic look or trade realism for altered semantics (weather, traffic lights, lanes); ψ-PD matches Ditto’s realism while keeping semantics intact. See Sec. 5.4. pearance. ψ-PD produces…
Figure 6
Figure 6. Figure 6: VLM planning on translated CARLA frames. Pre￾dicted waypoints (red) vs. ground-truth (green) [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Zero-shot instance-level video translation. The task requires only the character’s mirror reflection (highlighted in green in the first input frame) to appear photorealistic while the surrounding stylized scene remains untranslated. Ditto translates the entire frame in…
Figure 8
Figure 8. Figure 8: Ablation: Pareto frontiers (realism vs. consistency). photorealistic source, so it works beyond simulation [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

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Works this paper leans on

50 extracted references · 7 linked inside Pith

  1. [1]

    World simulation with video foun- dation models for physical ai.arXiv:2511.00062, 2025

    Arslan Ali, Junjie Bai, Maciej Bala, Yogesh Balaji, Aaron Blakeman, Tiffany Cai, Jiaxin Cao, Tianshi Cao, Elizabeth Cha, Yu-Wei Chao, et al. World simulation with video foun- dation models for physical ai.arXiv:2511.00062, 2025. 2, 3, 5, 6

    arXiv 2025

  2. [2]

    Scaling instruction-based video edit- ing with a high-quality synthetic dataset

    Qingyan Bai, Qiuyu Wang, Hao Ouyang, Yue Yu, Hanlin Wang, Wen Wang, Ka Leong Cheng, Shuailei Ma, Yanhong Zeng, Zichen Liu, et al. Scaling instruction-based video edit- ing with a high-quality synthetic dataset. InCVPR, 2026. 2, 3, 6

    2026

  3. [3]

    On the dual-tree com- plex wavelet packet andm-band transforms.IEEE Transac- tions on Signal Processing, 2008

    Ilker Bayram and Ivan W Selesnick. On the dual-tree com- plex wavelet packet andm-band transforms.IEEE Transac- tions on Signal Processing, 2008. 4

    2008

  4. [4]

    photo-concept-bucket

    bghira. photo-concept-bucket. Hugging Face Datasets, 2026. Accessed: 2026-03-04. 5

    2026

  5. [5]

    FLUX.https://github.com/ black- forest- labs/flux, 2024

    Black Forest Labs. FLUX.https://github.com/ black- forest- labs/flux, 2024. Accessed: 2026- 03-04. 2, 5

    2024

  6. [6]

    Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv:2311.15127, 2023

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv:2311.15127, 2023. 2

    arXiv 2023

  7. [7]

    In- structpix2pix: Learning to follow image editing instructions

    Tim Brooks, Aleksander Holynski, and Alexei A Efros. In- structpix2pix: Learning to follow image editing instructions. InCVPR, 2023. 2

    2023

  8. [8]

    Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom

    Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom. nuScenes: A multi- modal dataset for autonomous driving. InCVPR, 2020. 5

    2020

Show all 50 references
  1. [9]

    All snow re- moved: Single image desnowing algorithm using hierarchi- cal dual-tree complex wavelet representation and contradict channel loss

    Wei-Ting Chen, Hao-Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I Chen, Jian-Jiun Ding, Sy-Yen Kuo, et al. All snow re- moved: Single image desnowing algorithm using hierarchi- cal dual-tree complex wavelet representation and contradict channel loss. InICCV, 2021. 3

    2021

  2. [10]

    PhD thesis, Apollo - University of Cambridge Repository, 2019

    Fergal Cotter.Uses of Complex Wavelets in Deep Convolu- tional Neural Networks. PhD thesis, Apollo - University of Cambridge Repository, 2019. 4

    2019

  3. [11]

    CARLA: An open urban driving simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. InCoRL, 2017. 2, 5

    2017

  4. [12]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. InICML, 2024. 2

    2024

  5. [13]

    cwdm: conditional wavelet diffusion mod- els for cross-modality 3d medical image synthesis

    Paul Friedrich, Alicia Durrer, Julia Wolleb, and Philippe C Cattin. cwdm: conditional wavelet diffusion mod- els for cross-modality 3d medical image synthesis. arXiv:2411.17203, 2024. 3

    2024 arXiv

  6. [14]

    Wdm: 3d wavelet diffusion mod- els for high-resolution medical image synthesis

    Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer, and Philippe C Cattin. Wdm: 3d wavelet diffusion mod- els for high-resolution medical image synthesis. InMICCAI workshop on deep generative models, 2024. 3

    2024

  7. [15]

    Virtual worlds as proxy for multi-object tracking anal- ysis.arXiv:1605.06457, 2016

    Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. Virtual worlds as proxy for multi-object tracking anal- ysis.arXiv:1605.06457, 2016. 2, 5

    2016 arXiv

  8. [16]

    Are we ready for autonomous driving? The KITTI vision benchmark suite

    Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. InCVPR, 2012. 2, 5

    2012

  9. [17]

    On the gibbs phe- nomenon and its resolution.SIAM review, 1997

    David Gottlieb and Chi-Wang Shu. On the gibbs phe- nomenon and its resolution.SIAM review, 1997. 3

    1997

  10. [18]

    Wavelet score-based generative modeling

    Florentin Guth, Simon Coste, Valentin De Bortoli, and Stephane Mallat. Wavelet score-based generative modeling. NeurIPS, 2022. 3

    2022

  11. [19]

    On the use of windows for harmonic anal- ysis with the discrete fourier transform.Proceedings of the IEEE, 1978

    Fredric J Harris. On the use of windows for harmonic anal- ysis with the discrete fourier transform.Proceedings of the IEEE, 1978. 3

    1978

  12. [20]

    Denoising diffu- sion probabilistic models.NeurIPS, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffu- sion probabilistic models.NeurIPS, 2020. 2

    2020

  13. [21]

    Wavedm: Wavelet-based diffusion models for image restora- tion.IEEE TMM, 2024

    Yi Huang, Jiancheng Huang, Jianzhuang Liu, Mingfu Yan, Yu Dong, Jiaxi Lv, Chaoqi Chen, and Shifeng Chen. Wavedm: Wavelet-based diffusion models for image restora- tion.IEEE TMM, 2024. 3

    2024

  14. [22]

    Vbench: Comprehensive bench- mark suite for video generative models

    Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive bench- mark suite for video generative models. InCVPR, 2024. 5

    2024

  15. [23]

    Vace: All-in-one video creation and editing

    Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu. Vace: All-in-one video creation and editing. InICCV, 2025. 2, 3, 6

    2025

  16. [24]

    Flowedit: Inversion-free text-based editing using pre-trained flow models

    Vladimir Kulikov, Matan Kleiner, Inbar Huberman- Spiegelglas, and Tomer Michaeli. Flowedit: Inversion-free text-based editing using pre-trained flow models. InICCV,

  17. [25]

    Flow matching for generative mod- eling

    Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximil- ian Nickel, and Matt Le. Flow matching for generative mod- eling. InICLR, 2023. 2

    2023

  18. [26]

    Flow straight and fast: Learning to generate and transfer data with rectified flow

    Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. InICLR, 2023. 2

    2023

  19. [27]

    A theory for multiresolution signal decomposition: the wavelet representation.IEEE TPAMI,

    Stephane G Mallat. A theory for multiresolution signal decomposition: the wavelet representation.IEEE TPAMI,

  20. [28]

    The importance of phase in signals.Proceedings of the IEEE, 1981

    Alan V Oppenheim and Jae S Lim. The importance of phase in signals.Proceedings of the IEEE, 1981. 2, 3

    1981

  21. [29]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. InICCV, 2023. 2

    2023

  22. [30]

    Wavelet diffusion models are fast and scalable image generators

    Hao Phung, Quan Dao, and Anh Tran. Wavelet diffusion models are fast and scalable image generators. InCVPR,

  23. [31]

    Zhijie Qiao, Zhong Cao, and Henry X. Liu. nuCarla: A nuscenes-style bird’s-eye view perception dataset for carla simulation.arXiv:2511.13744, 2025. 5

    2025

  24. [32]

    Zhijie Qiao, Haowei Li, Zhong Cao, and Henry X. Liu. LightEMMA: Lightweight end-to-end multimodal model for autonomous driving.arXiv:2505.00284, 2025. 5

    2025 arXiv

  25. [33]

    Richter, Hassan Abu AlHaija, and Vladlen Koltun

    Stephan R. Richter, Hassan Abu AlHaija, and Vladlen Koltun. Enhancing photorealism enhancement.IEEE TPAMI, 2021. 5

    2021

  26. [34]

    High-resolution image syn- thesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj¨orn Ommer. High-resolution image syn- thesis with latent diffusion models. InCVPR, 2022. 2

    2022

  27. [35]

    La- tent wavelet diffusion for ultra-high-resolution image syn- thesis

    Luigi Sigillo, Shengfeng He, and Danilo Comminiello. La- tent wavelet diffusion for ultra-high-resolution image syn- thesis. InICLR, 2026. 3

    2026

  28. [36]

    Deep unsupervised learning using nonequilibrium thermodynamics

    Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. InICML, 2015. 2

    2015

  29. [37]

    Generative modeling by es- timating gradients of the data distribution.NeurIPS, 2019

    Yang Song and Stefano Ermon. Generative modeling by es- timating gradients of the data distribution.NeurIPS, 2019. 2

    2019

  30. [38]

    Score-based generative modeling through stochastic differential equa- tions

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equa- tions. InICLR, 2021. 2

    2021

  31. [39]

    Jpeg2000: Image compression fundamentals, stan- dards and practice.Journal of Electronic Imaging, 2002

    David S Taubman, Michael W Marcellin, and Majid Rab- bani. Jpeg2000: Image compression fundamentals, stan- dards and practice.Journal of Electronic Imaging, 2002. 3

    2002

  32. [40]

    Wan: Open and advanced large-scale video generative models

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianx- iao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jin- gren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, P...

    2025 arXiv

  33. [41]

    Hunyuanvideo 1.5 technical report.arXiv:2511.18870, 2025

    Tencent Hunyuan Foundation Model Team. Hunyuanvideo 1.5 technical report.arXiv:2511.18870, 2025. 2

    2025 arXiv

  34. [42]

    Sv3d: Novel multi-view syn- thesis and 3d generation from a single image using latent video diffusion

    Vikram V oleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view syn- thesis and 3d generation from a single image using latent video diffusion. InECCV, 2024. 2

    2024

  35. [43]

    Hiwave: Training-free high-resolution im- age generation via wavelet-based diffusion sampling

    Tobias V ontobel, Seyedmorteza Sadat, Farnood Salehi, and Romann Weber. Hiwave: Training-free high-resolution im- age generation via wavelet-based diffusion sampling. InPro- ceedings of the SIGGRAPH Asia 2025 Conference Papers,

    2025

  36. [44]

    Chan, and Chen Change Loy

    Jianyi Wang, Kelvin C.K. Chan, and Chen Change Loy. CLIP-IQA: Exploring CLIP for assessing the subjective and objective quality of images. InAAAI, 2023. 5

    2023

  37. [45]

    Dnaedit: Direct noise alignment for text-guided rectified flow editing.NeurIPS, 2026

    Chenxi Xie, Minghan Li, Shuai Li, Yuhui Wu, Qiaosi Yi, and Lei Zhang. Dnaedit: Direct noise alignment for text-guided rectified flow editing.NeurIPS, 2026. 2, 3, 5, 6

    2026

  38. [46]

    Segformer: Simple and ef- ficient design for semantic segmentation with transformers

    Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and ef- ficient design for semantic segmentation with transformers. InNeurIPS, 2021. 5

    2021

  39. [47]

    Neuralremaster: Phase-preserving diffusion for structure-aligned generation

    Yu Zeng, Charles Ochoa, Mingyuan Zhou, Vishal M Pa- tel, Vitor Guizilini, and Rowan McAllister. Neuralremaster: Phase-preserving diffusion for structure-aligned generation. arXiv:2512.05106, 2025. 2, 3, 4, 5, 6

    2025

  40. [48]

    open-sora-pexels-subset

    zengxianyu. open-sora-pexels-subset. Hugging Face Datasets, 2026. Accessed: 2026-03-04. 5

    2026

  41. [49]

    Gaussiancube: Structuring gaussian splatting using optimal transport for 3d generative modeling.NeurIPS, 2024

    Bowen Zhang, Yiji Cheng, Jiaolong Yang, Chunyu Wang, Feng Zhao, Yansong Tang, Dong Chen, and Baining Guo. Gaussiancube: Structuring gaussian splatting using optimal transport for 3d generative modeling.NeurIPS, 2024. 2

    2024

  42. [50]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In ICCV, 2023. 2

    2023

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.