Pith. sign in

REVIEW 3 major objections 2 minor 4 cited by

On the Astrophysical Origin of Binary Black Hole Subpopulations: A Tale of Three Channels?

T0 review · 3 major / 2 minor · reviewed 2026-07-13 · grok-4.5

Pith's one-line read Observed binary black hole mergers come from three channels—mostly isolated binaries—with fractions that change over cosmic time.

desk verdict Abstract-only three-channel BBH claim; the supplied full text is the wrong paper, so the channel mapping cannot be audited. read the letter →

arxiv 2603.17987 v2 pith:56QNF2GS submitted 2026-03-18 astro-ph.HE astro-ph.GAgr-qc

classification astro-ph.HEastro-ph.GAgr-qc
keywords binaryblackholesgravitationalwavesformationchannelsisolatedevolutiondynamicalassemblyhierarchicalmergerspopulationinference
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

Gravitational-wave catalogs show structure in black-hole masses, spins and redshifts that a single formation route cannot explain. Using mixture models that do not hard-wire any one channel, this work finds three subpopulations whose joint mass-ratio, spin-alignment, precession and redshift properties line up with isolated binary evolution, dynamical assembly in dense star clusters, and higher-generation mergers. The underlying fractions are roughly 79 percent, 14 percent and 3 percent, and those fractions themselves evolve with redshift at more than 1-sigma significance. Mass features that had previously been reported separately—the 10-solar-mass peak and the 35-solar-mass bump—emerge automatically as the distinct signatures of these channels. The result offers a simple, observationally driven map of how black-hole binaries form and when each channel dominates.

What carries the argument

Parametrized mixture models that let mass, mass-ratio, spin-alignment, spin-precession and redshift distributions vary freely across components; the three components are then matched to formation channels by simple, robust theoretical expectations rather than by full population-synthesis simulations.

What would settle it

A larger gravitational-wave catalog in which the component that peaks near 35 solar masses shows the same mass-ratio and spin-alignment distribution as the 10-solar-mass peak, or in which the relative fractions show no redshift evolution above 1-sigma.

Watch

Extended reading notes

Core claim

The current LIGO-Virgo-KAGRA binary-black-hole sample comprises three astrophysical subpopulations whose mass, mass-ratio, spin and redshift properties are consistent with relative underlying abundances of 79.0^{+11.5}_{-10.9}% isolated binary evolution, 14.5^{+11.6}_{-8.0}% dynamical formation in globular clusters, and 2.5^{+5.5}_{-1.8}% higher-generation mergers, with those fractions evolving over cosmic time.

Load-bearing premise

That the three mixture components can be identified with isolated binaries, cluster dynamics and higher-generation mergers solely on the basis of simple theoretical signatures that remain valid despite large uncertainties in stellar physics and selection effects.

Editorial extensions

If this is right

  • Future catalogs should show the isolated-binary fraction declining and the dynamical or higher-generation fraction rising at higher redshift.
  • Mass-based transitions already reported in spin and mass-ratio distributions are natural consequences of channel mixing and need not be modelled as separate breaks.
  • The small higher-generation component predicts a handful of events with both high mass and measurable spin-precession that will be identifiable in the next observing runs.
  • Relative channel abundances can be tracked as a function of redshift without waiting for full end-to-end population synthesis.

Reading between the lines

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

  • If the three-channel picture holds, rate measurements at z > 1 will become a direct probe of the relative efficiency of cluster versus field formation.
  • The same mixture framework can be re-applied to neutron-star–black-hole and binary-neutron-star samples once sample sizes permit, testing whether the same channels dominate.
  • A null detection of redshift evolution in the fractions would force either a revision of the channel assignments or a stronger role for selection effects than currently assumed.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The abstract claims that parametrized mixture models applied to the LIGO-Virgo-KAGRA BBH catalog reveal three astrophysical subpopulations, identified with isolated binary evolution (~79%), dynamical formation in globular clusters (~14.5%), and higher-generation mergers (~2.5%), with those relative fractions evolving over cosmic time at >1σ. It further asserts that the 10 M⊙ peak and 35 M⊙ feature have distinct mass-ratio, spin-alignment, spin-precession and redshift properties, and that previously reported mass-based transitions emerge naturally from the multi-component fit without explicit transition modeling. The interpretation is said to rest on simple theoretical predictions that are mostly robust to formation uncertainties.

Significance. If the channel-to-component mapping and the reported fractions (including redshift evolution) are robust, the result would be a high-impact, quantitative constraint on the relative contributions of the three dominant BBH formation channels and would help resolve the origin of the mass-spectrum features. The claim that mass-based transitions arise without explicit modeling would also be a useful methodological contribution. These conclusions cannot, however, be assessed from the materials supplied for review.

major comments (3)
  1. The full manuscript text supplied under paper_id 2603.17987 is not the BBH population paper described by the title and abstract. It is instead the unrelated computer-vision manuscript “Versatile Editing of Video Content, Actions, and Dynamics without Training” (DynaEdit). Consequently the parametrized mixture likelihood, selection-function treatment, spin and redshift conditional distributions, prior choices, and any quantitative comparison to population-synthesis predictions are entirely uninspectable. The central claim that the three mixture components are dominated by the three named channels therefore cannot be verified or falsified.
  2. Even taking the abstract at face value, the load-bearing step is the identification of mixture components with isolated binary evolution, globular-cluster dynamical formation, and higher-generation mergers via “simple theoretical predictions that are mostly robust against uncertainties.” Without the full text one cannot determine whether this mapping is a pure posterior summary or is partly enforced by the model structure, nor whether alternative channels that produce overlapping mass/spin/redshift signatures have been considered. That identification converts mixture weights into astrophysical abundances and is therefore essential to the strongest claim.
  3. The abstract reports that relative channel fractions evolve over cosmic time with more than 1σ confidence and that mass-based transitions “naturally emerge \ldots without explicit modeling.” Both statements require inspection of the hierarchical model, the redshift-dependent mixture weights, and the selection function; none of these elements appear in the supplied full text. Until the correct manuscript is provided, these results remain un-auditable.
minor comments (2)
  1. Once the correct manuscript is supplied, the abstract’s asymmetric uncertainties on the channel fractions should be checked for consistency with the full posterior (including selection effects and possible label-switching among mixture components).
  2. The abstract uses both “subpopulations” and “channels”; a clear statement of whether the mixture components are purely phenomenological or are given channel-specific parametric forms would aid readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be exhibited: full text of 2603.17987 is missing (wrong manuscript supplied), and the abstract alone shows no by-construction reduction.

full rationale

The load-bearing claim of arXiv:2603.17987 is a three-component mixture whose weights are interpreted as isolated binary evolution (~79%), globular-cluster dynamical (~14.5%), and higher-generation (~2.5%) channels, with >1σ redshift evolution. The only text actually belonging to that paper is the abstract. The CACHEABLE full manuscript is the unrelated DynaEdit video-editing paper (arXiv:2603.17989). Hard rule 1 forbids claiming circularity without a quoted reduction (Eq. X = Eq. Y by construction, or a fitted parameter renamed as a prediction). The abstract states that components are mapped to channels via 'simple theoretical predictions that are mostly robust against uncertainties,' but supplies no likelihood, mixture design, selection-effect treatment, or self-citation chain that can be reduced. Interpretive mapping of fitted components to named channels is a scientific risk, not a demonstrated circular step. With no inspectable derivation, steps is empty and the score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

Abstract-only ledger. The central claim rests on (i) a phenomenological mixture model whose component count and functional forms are free modeling choices, (ii) the domain assumption that observed mass/spin/redshift features map cleanly onto three standard formation channels, and (iii) the numerical fractions themselves as fitted free parameters. No new physical entities are invented; the 'channels' are standard labels.

free parameters (2)
  • relative channel fractions (isolated / dynamical / higher-generation) = 79.0^{+11.5}_{-10.9}%, 14.5^{+11.6}_{-8.0}%, 2.5^{+5.5}_{-1.8}%
    Quoted posterior 79.0^{+11.5}_{-10.9}%, 14.5^{+11.6}_{-8.0}%, 2.5^{+5.5}_{-1.8}% are fitted mixture weights, not derived from first principles.
  • mixture-component mass, mass-ratio, spin, and redshift hyperparameters
    Abstract states parametrized mixture models; the detailed shapes and any transition locations are free parameters of the fit (exact count unknown without full text).
assumptions (3)
  • domain assumption Observed BBH catalog features (10 Msun peak, 35 Msun feature, mass-ratio/spin/redshift trends) can be decomposed into a small number of astrophysical subpopulations via mixture models.
    Stated as the modeling premise of the Letter; component count and forms are not derived from stellar physics.
  • domain assumption Simple theoretical predictions for isolated binary evolution, globular-cluster dynamics, and higher-generation mergers are sufficiently robust to identify mixture components with those channels despite known uncertainties in binary stellar evolution, core collapse, and host environments.
    Explicitly claimed in the abstract as the basis for the channel interpretation.
  • ad hoc to paper Mass-based transitions in BBH parameter distributions emerge from the inferred multi-component distributions without needing explicit transition modeling.
    Presented as a result of the fit; treated as supporting evidence for the mixture rather than an independent axiom, but used to bolster the interpretation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of On the Astrophysical Origin of Binary Black Hole Subpopulations: A Tale of Three Channels?." pith.science (2026). https://pith.science/paper/56QNF2GS

@misc{pith2026260317987,
  author       = {Pith},
  title        = {Pith review of: On the Astrophysical Origin of Binary Black Hole Subpopulations: A Tale of Three Channels?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/56QNF2GS}},
  note         = {Machine review of arXiv:2603.17987}
}
abstract

There is increasing evidence for multiple binary black hole~(BBH) subpopulations in the cumulative gravitational wave catalog by the LIGO-Virgo-KAGRA Collaboration. The astrophysical interpretation of this complex underlying population is subject to theoretical uncertainties in treatments of binary stellar evolution, core collapse, and host environments. In this \textit{Letter}, using parametrized mixture models, we show that the BBH detection sample comprises three astrophysical subpopulations that are likely dominated by specific formation channels. In particular, we show that the $10M_{\odot}$ peak and the $35M_{\odot}$ feature in the BBH mass spectrum correspond to distinct mass-ratio, spin alignment, spin precession, and redshift evolution properties. We show that mass-based transitions reported in the distribution of BBH parameters naturally emerge from our inferred distributions without explicit modeling. Our results are consistent with the current observed population arising from specific relative abundances of isolated binary evolution, dynamical formation in globular clusters, and higher-generation BBH mergers. Under this interpretation, we constrain the relative underlying fraction of these channels to be $79.0^{+11.5}_{-10.9}\%$, $14.5^{+11.6}_{-8.0}\%$, and, $2.5^{+5.5}_{-1.8}\%$, respectively, and find these relative fractions to be evolving over cosmic time with more than $1\sigma$ confidence. Our interpretation relies on simple theoretical predictions that are mostly robust against uncertainties in BBH formation, with more definite conclusions expected in the near future.

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Four-dimensional Model-agnostic Probe into the Astrophysical Origins of Binary Black Hole Subpopulations

    astro-ph.HE 2026-07 conditional novelty 7.0 of 10

    A GPU-accelerated binned Gaussian process yields the first model-agnostic 4D BBH population in (m1, q, χeff, χp), revealing four mass-based subpopulations and new spin-mass-ratio correlations.

  2. High-mass binary black hole mergers from detailed binary evolution models

    astro-ph.HE 2026-07 conditional novelty 6.0 of 10

    Fully-conservative BH accretion is disfavored for high-mass BBH mergers; Eddington/GRRMHD accretion with kicks can match part of the LVK high-mass population but still needs another channel.

  3. Uncovering Hierarchical Sub-Population of Binary Black Holes

    astro-ph.HE 2026-07 conditional novelty 6.0 of 10

    A flexible six-component fit to 259 LIGO/Virgo/KAGRA black-hole mergers finds a roughly geometric sequence of mass peaks but no aligned-spin signal except in the lowest-mass component.

  4. The first decade of gravitational-wave measurements of black hole spins

    gr-qc 2026-06 unverdicted novelty 1.0 of 10

    A review summarizing formation-channel predictions, waveform effects, and population-level constraints on stellar-mass black hole spins from the first decade of gravitational-wave observations.

Reference graph

Works this paper leans on

57 extracted references · 5 linked inside Pith · cited by 4 Pith papers

  1. [1]

    Albergo,M.S.,Boffi,N.M.,Vanden-Eijnden,E.:Stochasticinterpolants:Aunifying framework for flows and diffusions (2025),��������������������������������

  2. [2]

    Bai, C., Shao, Z., Zhang, G., Liang, D., Yang, J., Zhang, Z., Guo, Y., Zhong, C., Qiu, Y., Wang, Z., Guan, Y., Zheng, X., Wang, T., Lu, C.: Anything in any scene: Photorealistic video object insertion (2024),��������������������������������

  3. [3]

    Bar-Tal, O., Chefer, H., Tov, O., Herrmann, C., Paiss, R., Zada, S., Ephrat, A., Hur, J., Liu, G., Raj, A., Li, Y., Rubinstein, M., Michaeli, T., Wang, O., Sun, D., Dekel, T., Mosseri, I.: Lumiere: A space-time diffusion model for video generation (2024),��������������������������������

  4. [4]

    Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S.W., Fidler, S., Kreis, K.: Align your latents: High-resolution video synthesis with latent diffusion models (2023),��������������������������������

  5. [5]

    Burgert, R., Herrmann, C., Cole, F., Ryoo, M.S., Wadhwa, N., Voynov, A., Ruiz, N.: Motionv2v: Editing motion in a video (2025),��������������������������� �����

  6. [6]

    Burgert, R., Xu, Y., Xian, W., Pilarski, O., Clausen, P., He, M., Ma, L., Deng, Y., Li, L., Mousavi, M., Ryoo, M., Debevec, P., Yu, N.: Go-with-the-flow: Motion- controllable video diffusion models using real-time warped noise (2025),������ ��������������������������

  7. [7]

    Ceylan, D., Huang, C.H.P., Mitra, N.J.: Pix2video: Video editing using image diffusion (2023),��������������������������������

  8. [8]

    Cohen, N., Kulikov, V., Kleiner, M., Huberman-Spiegelglas, I., Michaeli, T.: Slicedit: Zero-shot video editing with text-to-image diffusion models using spatio- temporal slices (2024),��������������������������������

Show all 57 references
  1. [9]

    Cong, Y., Xu, M., Simon, C., Chen, S., Ren, J., Xie, Y., Perez-Rua, J.M., Rosen- hahn, B., Xiang, T., He, S.: Flatten: optical flow-guided attention for consistent text-to-video editing (2024),��������������������������������

  2. [10]

    arXiv preprint arXiv:2506.10082 (2025)

    Gao, C., Ding, L., Cai, X., Huang, Z., Wang, Z., Xue, T.: Lora-edit: Controllable first-frame-guided video editing via mask-aware lora fine-tuning. arXiv preprint arXiv:2506.10082 (2025)

  3. [11]

    Garibi,D.,Yadin,S.,Paiss,R.,Tov,O.,Zada,S.,Ephrat,A.,Michaeli,T.,Mosseri, I., Dekel, T.: Tokenverse: Versatile multi-concept personalization in token modu- lation space (2025),��������������������������������

  4. [12]

    Geyer, M., Bar-Tal, O., Bagon, S., Dekel, T.: Tokenflow: Consistent diffusion fea- tures for consistent video editing (2023),��������������������������������

  5. [13]

    Google DeepMind: Veo: Our most capable generative video model (2024),������ �����������������, accessed: 2026-02-18

  6. [14]

    HaCohen, Y., Brazowski, B., Chiprut, N., Bitterman, Y., Kvochko, A., Berkowitz, A., Shalem, D., Lifschitz, D., Moshe, D., Porat, E., Richardson, E., Shiran, G., Chachy, I., Chetboun, J., Finkelson, M., Kupchick, M., Zabari, N., Guetta, N., Kotler, N., Bibi, O., Gordon, O., Pan...

  7. [15]

    arXiv preprint arXiv:2501.00103 (2024) Versatile Editing of Video Content, Actions, and Dynamics without Training 17

    HaCohen, Y., Chiprut, N., Brazowski, B., Shalem, D., Moshe, D., Richardson, E., Levin, E., Shiran, G., Zabari, N., Gordon, O., Panet, P., Weissbuch, S., Kulikov, V., Bitterman, Y., Melumian, Z., Bibi, O.: Ltx-video: Realtime video latent diffusion. arXiv preprint arXiv:2501.00...

  8. [16]

    Ho, J., Salimans, T.: Classifier-free diffusion guidance (2022),������������������ ��������������

  9. [17]

    Hsu, H.Y., Lin, Z.H., Zhai, A., Xia, H., Wang, S.: Autovfx: Physically realistic video editing from natural language instructions (2024),���������������������� ����������

  10. [18]

    Huberman-Spiegelglas, I., Kulikov, V., Michaeli, T.: An edit friendly ddpm noise space: Inversion and manipulations (2024),��������������������������������

  11. [19]

    Jiang, Z., Han, Z., Mao, C., Zhang, J., Pan, Y., Liu, Y.: Vace: All-in-one video creation and editing (2025),��������������������������������

  12. [20]

    Jones, M., Abdal, R., Patashnik, O., Salakhutdinov, R., Tulyakov, S., Zhu, J.Y., Wang, K.C.J.: Tuning-free visual effect transfer across videos (2026),�������� ������������������������

  13. [21]

    Kara, O., Kurtkaya, B., Yesiltepe,H., Rehg, J.M., Yanardag, P.:Rave: Randomized noise shuffling for fast and consistent video editing with diffusion models (2023), ��������������������������������

  14. [22]

    Kim, J., Hong, Y., Park, J., Ye, J.C.: Flowalign: Trajectory-regularized, inversion- free flow-based image editing (2025),��������������������������������

  15. [23]

    arXiv preprint arXiv:2412.03603 (2024)

    Kong, W., Tian, Q., Zhang, Z., Min, R., Dai, Z., Zhou, J., Xiong, J., Li, X., Wu, B., Zhang, J., et al.: Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603 (2024)

  16. [24]

    arXiv preprint arXiv:2403.14468 (2024)

    Ku, M., Wei, C., Ren, W., Yang, H., Chen, W.: Anyv2v: A tuning-free framework for any video-to-video editing tasks. arXiv preprint arXiv:2403.14468 (2024)

  17. [25]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Kulikov, V., Kleiner, M., Huberman-Spiegelglas, I., Michaeli, T.: Flowedit: Inversion-free text-based editing using pre-trained flow models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19721–19730 (2025)

  18. [26]

    arXiv preprint arXiv: 2506.05046 (2025)

    Li, G., Yang, Y., Song, C., Zhang, C.: Flowdirector: Training-free flow steering for precise text-to-video editing. arXiv preprint arXiv: 2506.05046 (2025)

  19. [27]

    Lipman, Y., Chen, R.T.Q., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling (2023),��������������������������������

  20. [28]

    Liu, S., Zhang, Y., Li, W., Lin, Z., Jia, J.: Video-p2p: Video editing with cross- attention control (2023),��������������������������������

  21. [29]

    Liu, X., Gong, C., Liu, Q.: Flow straight and fast: Learning to generate and transfer data with rectified flow (2022),��������������������������������

  22. [30]

    Mehraban,S.,Adeli,V.,Rommann,J.,Taati,B.,Truskovskyi,K.:Pickstyle:Video- to-video style transfer with context-style adapters (2025),������������������ ��������������

  23. [31]

    Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations (2022),������ ��������������������������

  24. [32]

    Meral, T.H.S., Yesiltepe, H., Dunlop, C., Yanardag, P.: Motionflow: Attention- driven motion transfer in video diffusion models (2024),���������������������� ����������

  25. [33]

    Motamed, S., Culp, L., Swersky, K., Jaini, P., Geirhos, R.: Do generative video models understand physical principles? (2025),��������������������������� �����

  26. [34]

    OpenAI: Sora 2: Our flagship video and audio generation model.��������������� ���(2025), accessed: 2026-02-25

  27. [35]

    arXiv preprint arXiv:2405.16537 (2024) 18 V

    Ouyang, W., Dong, Y., Yang, L., Si, J., Pan, X.: I2vedit: First-frame-guided video editing via image-to-video diffusion models. arXiv preprint arXiv:2405.16537 (2024) 18 V. Kulikov et al

  28. [36]

    In: The Thirteenth International Conference on Learning Representa- tions (2025),������������������������������������������

    Peng, Y., Cui, Y., Tang, H., Qi, Z., Dong, R., Bai, J., Han, C., Ge, Z., Zhang, X., Xia, S.T.: Dreambench++: A human-aligned benchmark for personalized image generation. In: The Thirteenth International Conference on Learning Representa- tions (2025),��������������������������...

  29. [37]

    Pexels GmbH: Pexels: Free stock photos, royalty free images & videos.������ ����������������(2024), accessed: 2024-05-22

  30. [38]

    Polaczek, S., Patashnik, O., Mahdavi-Amiri, A., Cohen-Or, D.: In-context sync- lora for portrait video editing (2025),��������������������������������

  31. [39]

    Pondaven, A., Siarohin, A., Tulyakov, S., Torr, P., Pizzati, F.: Video motion trans- fer with diffusion transformers (2025),��������������������������������

  32. [40]

    Qi, C., Cun, X., Zhang, Y., Lei, C., Wang, X., Shan, Y., Chen, Q.: Fatezero: Fusing attentions for zero-shot text-based video editing (2023),���������������������� ����������

  33. [41]

    Runway AI, Inc.: Introducing Runway Aleph: A state-of-the-art in-context video model (2025),������������������������������������������������������, ac- cessed: 2026-02-18

  34. [42]

    Singer, U., Zohar, A., Kirstain, Y., Sheynin, S., Polyak, A., Parikh, D., Taigman, Y.: Video editing via factorized diffusion distillation (2024),������������������ ��������������

  35. [43]

    Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models (2022),������ ��������������������������

  36. [44]

    Tewel,Y.,Gal,R.,Samuel,D.,Atzmon,Y.,Wolf,L.,Chechik,G.:Add-it:Training- free object insertion in images with pretrained diffusion models (2024),������ ��������������������������

  37. [45]

    Tu, Y., Luo, H., Chen, X., Ji, S., Bai, X., Zhao, H.: Videoanydoor: High-fidelity video object insertion with precise motion control (2025),������������������ ��������������

  38. [46]

    arXiv preprint arXiv:2503.20314 (2025)

    Wan, T., Wang, A., Ai, B., Wen, B., Mao, C., Xie, C.W., Chen, D., Yu, F., Zhao, H., Yang, J., Zeng, J., Wang, J., Zhang, J., Zhou, J., Wang, J., Chen, J., Zhu, K., Zhao, K., Yan, K., Huang, L., Feng, M., Zhang, N., Li, P., Wu, P., Chu, R., Feng, R., Zhang, S., Sun, S., Fang, T...

  39. [47]

    Wang, J., Pu, J., Qi, Z., Guo, J., Ma, Y., Huang, N., Chen, Y., Li, X., Shan, Y.: Taming rectified flow for inversion and editing (2025),���������������������� ����������

  40. [48]

    Wang, Y., Wang, L., Ma, Z., Hu, Q., Xu, K., Guo, Y.: Videodirector: Precise video editing via text-to-video models (2025),��������������������������������

  41. [49]

    Wiedemer, T., Li, Y., Vicol, P., Gu, S.S., Matarese, N., Swersky, K., Kim, B., Jaini, P., Geirhos, R.: Video models are zero-shot learners and reasoners (2025), ��������������������������������

  42. [50]

    Wu, J.Z., Ge, Y., Wang, X., Lei, W., Gu, Y., Shi, Y., Hsu, W., Shan, Y., Qie, X., Shou, M.Z.: Tune-a-video: One-shot tuning of image diffusion models for text-to- video generation (2023),��������������������������������

  43. [51]

    2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp

    Wu, R., Gao, R., Poole, B., Trevithick, A., Zheng, C., Barron, J.T., Holynski, A.: Cat4d: Create anything in 4d with multi-view video diffusion models. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 26057–26068 (2024),���������������������������...

  44. [52]

    Yang, S., Zhou, Y., Liu, Z., Loy, C.C.: Rerender a video: Zero-shot text-guided video-to-video translation (2023),��������������������������������

  45. [53]

    Yang, Z., Teng, J., Zheng, W., Ding, M., Huang, S., Xu, J., Yang, Y., Hong, W., Zhang, X., Feng, G., Yin, D., Zhang, Y., Wang, W., Cheng, Y., Xu, B., Gu, X., Dong, Y., Tang, J.: Cogvideox: Text-to-video diffusion models with an expert transformer (2025),�����������������������...

  46. [54]

    Yatim, D., Fridman, R., Bar-Tal, O., Dekel, T.: Dynvfx: Augmenting real videos with dynamic content (2025),��������������������������������

  47. [55]

    Yatim, D., Fridman, R., Bar-Tal, O., Kasten, Y., Dekel, T.: Space-time diffusion features for zero-shot text-driven motion transfer (2023),������������������ ��������������

  48. [56]

    Ye, Z., Huang, H., Wang, X., Wan, P., Zhang, D., Luo, W.: Stylemaster: Stylize your video with artistic generation and translation (2024),������������������ ��������������

  49. [57]

    Two astronauts are walking on Mars. Desert, mountains in the background. The camera follows them

    Zhang, D.J., Paiss, R., Zada, S., Karnad, N., Jacobs, D.E., Pritch, Y., Mosseri, I., Shou, M.Z., Wadhwa, N., Ruiz, N.: Recapture: Generative video camera controls for user-provided videos using masked video fine-tuning. 2025 IEEE/CVF Confer- ence on Computer Vision and Pattern...

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.