Pith. sign in

REVIEW 3 major objections 6 minor 71 references

Audio-Sync Video Generation with Multi-Stream Temporal Control

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read By separating audio into speech, effects, and music, MTV makes audio-sync video generation precisely controllable.

desk verdict A genuinely novel architecture and a valuable dataset undercut by an unfair baseline comparison and a 50-video evaluation; the SOTA claim needs more support. read the letter →

arxiv 2506.08003 v1 pith:NZNIPVSQ submitted 2025-06-09 cs.CV cs.AI

Shuchen Weng , Haojie Zheng , Zheng Chang , Si Li , Boxin Shi , Xinlong Wang This is my paper
classification cs.CVcs.AI
keywords audio-syncvideogenerationaudiodemixingmulti-streamtemporalcontrollipmotionsynchronizationeventtimingvisualmoodDEMIXdataset
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 introduces MTV, a video-generation framework whose central move is to separate an input audio into speech, effects, and music tracks, routing each track to a different visual control: lip motion, event timing, and visual mood. A sympathetic reader would care because prior audio-to-video models treat audio as a single condition and therefore produce scene-level matches but miss fine synchronization, such as lips matching words or a splash landing exactly when the sound plays. To train MTV, the paper contributes DEMIX, roughly 392K cinematic video clips with demixed audio tracks, organized into five overlapping subsets so the model can learn controls from concrete (lip motion) to global (visual mood) in stages. The reported result is state-of-the-art numbers on six metrics covering video quality, text-video consistency, and audio-video alignment.

What carries the argument

The central object is the Multi-Stream Temporal ControlNet (MST-ControlNet). It consists of an interval stream, which combines speech and effects embeddings through interval interaction blocks and injects them into matching time intervals via cross-attention, and a holistic stream, which encodes music into per-clip style scaling and shifting factors applied uniformly to all frames. This machinery converts an audio waveform into two distinct conditioning routes: one local and synchronous, and one global and atmospheric.

What would settle it

Retrain or finetune the baselines on the same DEMIX training data and re-run the six-metric evaluation; if the gap largely disappears, the reported state-of-the-art result reflects the training-data advantage rather than the multi-stream architecture itself.

Watch

Extended reading notes

Core claim

MTV claims that demixing audio before conditioning turns audio-to-video generation from an under-specified mapping into a set of disentangled, temporally precise controls. Wav2vec features from the speech and effects tracks are processed by an interval stream with cross-attention into per-time-interval video latents, driving lip motion and event timing, while music features are pooled by a holistic stream and injected as a style modulation across all frames, shaping the visual mood. Trained on the DEMIX dataset with a five-stage curriculum, the framework reports the best FVD, temporal consistency, text consistency, audio consistency, and lip-sync scores among the compared methods.

Load-bearing premise

The claim of state-of-the-art performance rests on the assumption that comparing MTV with baselines under their original configurations is a fair test, even though MTV is trained on 392K additional DEMIX clips while some baselines are not.

Editorial extensions

If this is right

  • If the reported numbers hold, the three audio tracks become independent controls: changing the speech track alters lip motion, changing effects alters event timing, and changing music alters visual mood without retraining.
  • The DEMIX dataset, with its demixed tracks and five overlapping subsets, provides a training resource and curriculum that other audio-video generation methods can reuse.
  • Because MTV builds on a pretrained text-to-video generator, it can combine text-specified scenes with audio-specified timing, enabling applications such as turning podcasts and historical recordings into visual narratives.
  • The interval-versus-holistic stream split gives a template for separating local synchronous controls from global ambient controls in other conditional generation tasks.
  • The reported six-metric gains imply that audio-visual synchronization can be evaluated and optimized as a multi-stream problem rather than as one holistic audio-conditioning problem.

Reading between the lines

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

  • A direct test the paper leaves implicit is whether the six-metric lead persists when MTV is trained without its 392K DEMIX advantage and the baselines are given comparable training data; this would separate the value of the architecture from the value of the dataset.
  • Because the paper states that the approach is limited by the categories provided by upstream audio demixing tools, an obvious extension is to couple MTV with more fine-grained demixers, such as those that separate multiple speakers or individual event sounds, and measure whether sync metrics improve accordingly.
  • The same speech-effects-music split could be applied to joint video-and-audio generation, where the generated audio could first be demixed and then used to control the generated video, potentially tightening synchronization without requiring pre-recorded audio.
  • A practical extension is to feed MTV a music track with a clearly annotated emotional arc and test whether the generated visual mood shifts at the annotated boundaries, which would isolate the holistic stream's contribution from the text-to-video prior.
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 / 6 minor

Summary. The paper proposes MTV, an audio-sync video generation framework that demixes input audio into speech, effects, and music tracks and uses a proposed Multi-Stream Temporal ControlNet (MST-ControlNet) to control lip motion, event timing, and visual mood respectively. It also introduces DEMIX, a dataset of 392K cinematic video clips with demixed audio tracks, structured into five overlapped subsets for a multi-stage training strategy. The paper claims state-of-the-art performance across six metrics (FVD, Temp-C, Text-C, Audio-C, Sync-C, Sync-D) in comparison with three recent baselines (MM-Diffusion, TempoTokens, Xing et al.), and reports ablations supporting the value of the proposed modules. The central claim is that MTV achieves state-of-the-art audio-sync video generation.

Significance. If the results are established, the DEMIX dataset is a substantial new resource for audio-sync video generation, and the architectural idea of separately controlling lip motion, event timing, and visual mood via demixed audio tracks is a plausible and potentially useful direction. The paper also ships a clear multi-stage training strategy and quantitative ablations with named components. However, the experimental evidence as presented is not sufficient to support the headline state-of-the-art claim because the baseline comparison is confounded by training data and conditioning, and the evaluation set is too small to support reliable conclusions. The strengths of the dataset and architecture are real, but the core empirical claim requires a controlled comparison and more rigorous statistics.

major comments (3)
  1. [Sec. 5.1, Table 2] The comparison with baselines is not a fair test of method quality. MTV is trained on 392K DEMIX video clips with demixed audio tracks and structured text captions, while TempoTokens and Xing et al. are evaluated in their original off-the-shelf configuration, and MM-Diffusion is only finetuned without a description of the data used for finetuning. The reported improvements, especially the very large margins on Audio-C (26.22 vs. 7.30) and Sync-C (3.17 vs. 1.55), may therefore reflect a training-data and conditioning-format advantage rather than the MST-ControlNet design. To support the state-of-the-art claim, the authors should either retrain all baselines on DEMIX with the same demixed conditioning and text structure, or evaluate MTV under each baseline's training setup, and report the results of such a controlled comparison.
  2. [Sec. 5.1] Only 50 videos are randomly selected from the testing set for evaluation, and no error bars, confidence intervals, or significance tests are reported. Frechet Video Distance computed on 50 samples is known to have high variance, and all six metrics are affected by the small sample. Without repeated evaluation seeds or a larger evaluation set, it is impossible to tell whether the reported differences between methods are statistically meaningful, even if the protocol were otherwise fair. The authors should increase the number of evaluation videos and report mean and standard deviation or confidence intervals.
  3. [Sec. 7.2, Table 3] The paper states that AV-Align is unsuitable because real videos score lowest with that metric, yet the abstract and Section 5.1 claim state-of-the-art performance across six metrics, including audio-video alignment metrics. If AV-Align is demonstrably broken for this task, that raises a concern about the validity of the other alignment metrics (Sync-C/Sync-D, Audio-C) as well. The authors should either justify why the remaining alignment metrics are reliable, provide an alternative alignment evaluation (e.g., human study or a metric known to correlate with real videos), or temper the claim about alignment performance. As written, the acknowledged failure of one alignment metric weakens confidence in the alignment results.
minor comments (6)
  1. [Sec. 3] The dataset statistics say the five subsets are 'overlapped' but then report 'non-overlapped 392K clips'; please clarify whether the total is the union size after deduplication or the sum of subset sizes.
  2. [Sec. 5.1] The finetuning procedure for MM-Diffusion is not described: it is unclear what data, number of steps, and hyperparameters were used for the 'over 320K steps' finetuning mentioned in the qualitative comparisons. This information is needed to assess whether that baseline was given a comparable training opportunity.
  3. [Sec. 5.2] The ablation section refers to 'Fig. 6' for the ablation results, but the ablation figure appears to be Fig. 4 in the manuscript; please correct the cross-reference.
  4. [Sec. 7.2] 'Frechét Video Distance' should be spelled 'Fréchet Video Distance'.
  5. [Eq. (2)] The notation for the interval stream is under-specified: please define the interval index i and the division of the latent code into intervals, and clarify how hs_i and he_i are combined when the speech and effects intervals may not align.
  6. [Sec. 6] The limitation section only mentions the scope of audio demixing categories; it would also be appropriate to note that errors in the demixing stage will propagate to the conditioned generation, since all downstream control depends on the quality of the separated tracks.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MTV's claim is a standard supervised training result evaluated with external metrics; comparison-fairness concerns are not circular.

full rationale

The paper's derivation chain is a standard supervised learning pipeline: MTV is trained on DEMIX with the diffusion noise-prediction loss (Eq. 1), and its outputs are evaluated with external metrics (FVD, CLIP-based Temp-C/Text-C, ImageBind Audio-C, Sync-C/Sync-D). No component of the loss or architecture is defined in terms of the evaluation metrics, and no fitted parameter is renamed as a prediction. The DEMIX dataset is constructed from external sources (CelebV-HQ, MovieBench, Condensed Movies, Short-Films 20K, YouTube) with filtering; the 1K held-out test clips come from the same distribution, which is standard practice rather than circular. The only author-overlap item is the baseline Xing et al. [3], which is used as a comparison method, not as a load-bearing justification; even if it is same-group work, it does not support any derivation step. The AV-Align metric caveat (Table 3: real videos score lowest) is a metric-validity disclosure, not a circular argument. The comparison unfairness (baselines not trained on DEMIX) is a real experimental-fairness risk but belongs to correctness, not circularity. Therefore no circular step is present.

Assumptions & free parameters 5 free parameters · 5 assumptions · 2 invented entities

The central claim relies on a standard supervised training setup. The model has no mathematically derived constants; all numbers are learned from data or set as hyperparameters. The main assumptions are that demixing tools produce reliable separated tracks, wav2vec features are sufficient for control, and the proposed metrics are valid. No new physical entities are introduced beyond the model and dataset.

free parameters (5)
  • Number of training steps per stage = 40K
    Training hyperparameter for each of five stages; not justified by analysis.
  • Learning rate = 1e-5
    Adam optimizer learning rate, chosen apparently by convention.
  • L1 distance threshold for demixing filtering = not specified
    Clips are discarded if 'high L1 distances' are found; the threshold is ad hoc and not quantified.
  • Silence threshold for music/effects = -45 dB
    Used to decide whether to compare Spleeter 'others' with effects or music track; value chosen without sensitivity analysis.
  • Number of evaluation videos = 50
    Random selection from 1K test set; no justification for sample size and no confidence intervals.
assumptions (5)
  • domain assumption Audio demixing tools (MVSEP, Spleeter) produce reliable separated speech, effects, and music tracks
    Section 3 'Demixing filtering' relies on these outputs to build training pairs; errors propagate to supervision.
  • domain assumption Wav2vec representations contain enough information to drive lip motion, event timing, and visual mood
    Section 4.2 'Audio encoding' uses wav2vec for all three tracks.
  • domain assumption Pretrained CogVideoX provides strong generative priors suitable for audio-sync control
    Section 4.1 builds on CogVideoX; if priors are text-only, audio conditioning may not transfer well.
  • domain assumption The interval interaction blocks can learn to assign audio features to correct time intervals
    Section 4.2 'Interval stream' assumes alignment is learnable from data.
  • domain assumption The selected metrics (FVD, Text-C, Audio-C, Sync-C/D) validly measure audio-visual synchronization for cinematic videos
    Section 5.1 and Appendix 7.2 adopt these metrics; AV-Align is discarded because real videos score low.
invented entities (2)
  • MST-ControlNet
    purpose: Multi-stream temporal control of video generation from demixed audio tracks
    Only in-paper ablations support its efficacy; no external validation or release.
  • DEMIX dataset
    purpose: Training and evaluation data of cinematic videos with demixed audio tracks
    Not publicly released at submission; only samples are shown on the project page.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Audio-Sync Video Generation with Multi-Stream Temporal Control." pith.science (2026). https://pith.science/paper/NZNIPVSQ

@misc{pith2026250608003,
  author       = {Pith},
  title        = {Pith review of: Audio-Sync Video Generation with Multi-Stream Temporal Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZNIPVSQ}},
  note         = {Machine review of arXiv:2506.08003}
}
read the original abstract

Audio is inherently temporal and closely synchronized with the visual world, making it a naturally aligned and expressive control signal for controllable video generation (e.g., movies). Beyond control, directly translating audio into video is essential for understanding and visualizing rich audio narratives (e.g., Podcasts or historical recordings). However, existing approaches fall short in generating high-quality videos with precise audio-visual synchronization, especially across diverse and complex audio types. In this work, we introduce MTV, a versatile framework for audio-sync video generation. MTV explicitly separates audios into speech, effects, and music tracks, enabling disentangled control over lip motion, event timing, and visual mood, respectively -- resulting in fine-grained and semantically aligned video generation. To support the framework, we additionally present DEMIX, a dataset comprising high-quality cinematic videos and demixed audio tracks. DEMIX is structured into five overlapped subsets, enabling scalable multi-stage training for diverse generation scenarios. Extensive experiments demonstrate that MTV achieves state-of-the-art performance across six standard metrics spanning video quality, text-video consistency, and audio-video alignment. Project page: https://hjzheng.net/projects/MTV/.

Figures

Figures reproduced from arXiv: 2506.08003 by the authors.

Figure 1
Figure 1. MTV demonstrates versatile audio-sync video generation capabilities following user [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The pipeline of our MTV framework. (a-c) MTV is built on a pretrained text-to-video [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Visual comparison results with state-of-the-art methods for audio-sync video generation. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Ablation study results of different MST-ControlNet variants. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Examples of versatile application scenarios for our proposed MTV framework. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Examples of controllability study for text descriptions and demixed audios. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Visual comparison results with state-of-the-art methods for talking human. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Dataset processing pipeline for our DEMIX dataset. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

71 extracted references · 44 canonical work pages

  1. [1]

    Diverse and aligned audio-to- video generation via text-to-video model adaptation,

    G. Yariv, I. Gat, S. Benaim, L. Wolf, I. Schwartz, and Y . Adi, “Diverse and aligned audio-to- video generation via text-to-video model adaptation,” in AAAI, 2024

    work page 2024

  2. [2]

    Long video generation with time-agnostic vqgan and time-sensitive transformer,

    S. Ge, T. Hayes, H. Yang, X. Yin, G. Pang, D. Jacobs, J.-B. Huang, and D. Parikh, “Long video generation with time-agnostic vqgan and time-sensitive transformer,” inECCV, 2022

    work page 2022

  3. [3]

    Seeing and hearing: Open-domain visual-audio generation with diffusion latent aligners,

    Y . Xing, Y . He, Z. Tian, X. Wang, and Q. Chen, “Seeing and hearing: Open-domain visual-audio generation with diffusion latent aligners,” in CVPR, 2024

    work page 2024

  4. [4]

    Sound-guided semantic video generation,

    S. H. Lee, G. Oh, W. Byeon, J. Bae, C. Kim, W. J. Ryoo, S. H. Yoon, J. Kim, and S. Kim, “Sound-guided semantic video generation,” in ECCV, 2022

    work page 2022

  5. [5]

    MM-Diffusion: Learning multi-modal diffusion models for joint audio and video generation,

    L. Ruan, Y . Ma, H. Yang, H. He, B. Liu, J. Fu, N. J. Yuan, Q. Jin, and B. Guo, “MM-Diffusion: Learning multi-modal diffusion models for joint audio and video generation,” in CVPR, 2023

    work page 2023

  6. [6]

    TA2V: Text-audio guided video generation,

    M. Zhao, W. Wang, T. Chen, R. Zhang, and R. Li, “TA2V: Text-audio guided video generation,” TMM, 2024

    work page 2024

  7. [7]

    Hallo3: Highly dynamic and realistic portrait image animation with video diffusion transformer,

    J. Cui, H. Li, Y . Zhan, H. Shang, K. Cheng, Y . Ma, S. Mu, H. Zhou, J. Wang, and S. Zhu, “Hallo3: Highly dynamic and realistic portrait image animation with video diffusion transformer,” in CVPR, 2025

    work page 2025

  8. [8]

    Speech drives templates: Co-speech gesture synthesis with learned templates,

    S. Qian, Z. Tu, Y . Zhi, W. Liu, and S. Gao, “Speech drives templates: Co-speech gesture synthesis with learned templates,” in ICCV, 2021

    work page 2021

Show all 71 references
  1. [9]

    Visualize music using generative arts,

    B. M.-K. Ng, S. R. Sudhoff, H. Li, J. Kamphuis, T. Nadolsky, Y . Chen, K. Y .-J. Yun, and Y .-H. Lu, “Visualize music using generative arts,” inCAI, 2024

    2024

  2. [10]

    CogVideox: Text-to-video diffusion models with an expert transformer,

    Z. Yang, J. Teng, W. Zheng, M. Ding, S. Huang, J. Xu, Y . Yang, W. Hong, X. Zhang, G. Feng, et al., “CogVideox: Text-to-video diffusion models with an expert transformer,” inICLR, 2025

    2025

  3. [11]

    Structure and content- guided video synthesis with diffusion models,

    P. Esser, J. Chiu, P. Atighehchian, J. Granskog, and A. Germanidis, “Structure and content- guided video synthesis with diffusion models,” in ICCV, 2023

    2023

  4. [12]

    Stable video diffusion: Scaling latent video diffusion models to large datasets,

    A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y . Levi, Z. English, V . V oleti, A. Letts,et al., “Stable video diffusion: Scaling latent video diffusion models to large datasets,” arXiv preprint arXiv:2311.15127, 2023

    2023 arXiv

  5. [13]

    Latent video diffusion models for high-fidelity long video generation,

    Y . He, T. Yang, Y . Zhang, Y . Shan, and Q. Chen, “Latent video diffusion models for high-fidelity long video generation,” arXiv preprint arXiv:2211.13221, 2022

    2022 arXiv

  6. [14]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR, 2022

    2022

  7. [15]

    Interpretable 3D human action analysis with temporal convolutional networks,

    T. Soo Kim and A. Reiter, “Interpretable 3D human action analysis with temporal convolutional networks,” in CVPR workshops, 2017

    2017

  8. [16]

    Is space-time attention all you need for video understanding?,

    G. Bertasius, H. Wang, and L. Torresani, “Is space-time attention all you need for video understanding?,” in ICML, 2021. 12

    2021

  9. [17]

    U-Net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in MICCAI, 2015

    2015

  10. [18]

    Sora: A review on background, technology, limitations, and opportunities of large vision models,

    Y . Liu, K. Zhang, Y . Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y . Huang, H. Sun, J. Gao,et al., “Sora: A review on background, technology, limitations, and opportunities of large vision models,” arXiv preprint arXiv:2402.17177, 2024

    2024 arXiv

  11. [19]

    Scalable diffusion models with transformers,

    W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in ICCV, 2023

    2023

  12. [20]

    Language model beats diffusion – tokenizer is key to visual generation,

    L. Yu, J. Lezama, N. B. Gundavarapu, L. Versari, K. Sohn, D. Minnen, Y . Cheng, V . Birodkar, A. Gupta, X. Gu, A. G. Hauptmann, B. Gong, M.-H. Yang, I. Essa, D. A. Ross, and L. Jiang, “Language model beats diffusion – tokenizer is key to visual generation,” inICLR, 2024

    2024

  13. [21]

    Wan: Open and advanced large-scale video generative models,

    A. Wang, B. Ai, B. Wen, C. Mao, C.-W. Xie, D. Chen, F. Yu, H. Zhao, J. Yang, J. Zeng, et al. , “Wan: Open and advanced large-scale video generative models,” arXiv preprint arXiv:2503.20314, 2025

    2025 arXiv

  14. [22]

    Hunyuanvideo: A systematic framework for large video generative models,

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

    2024 arXiv

  15. [23]

    Step-Video-T2V technical report: The practice, challenges, and future of video foundation model,

    G. Ma, H. Huang, K. Yan, L. Chen, N. Duan, S. Yin, C. Wan, R. Ming, X. Song, X. Chen,et al., “Step-Video-T2V technical report: The practice, challenges, and future of video foundation model,” arXiv preprint arXiv:2502.10248, 2025

    2025 arXiv

  16. [24]

    Sound2Sight: Generating visual dynamics from sound and context,

    M. Chatterjee and A. Cherian, “Sound2Sight: Generating visual dynamics from sound and context,” in ECCV, 2020

    2020

  17. [25]

    CCVS: Context-aware controllable video synthesis,

    G. Le Moing, J. Ponce, and C. Schmid, “CCVS: Context-aware controllable video synthesis,” in NeurIPS, 2021

    2021

  18. [26]

    The power of sound (TPoS): Audio reactive video generation with stable diffusion,

    Y . Jeong, W. Ryoo, S. Lee, D. Seo, W. Byeon, S. Kim, and J. Kim, “The power of sound (TPoS): Audio reactive video generation with stable diffusion,” inICCV, 2023

    2023

  19. [27]

    Audio-synchronized visual animation,

    L. Zhang, S. Mo, Y . Zhang, and P. Morgado, “Audio-synchronized visual animation,” inECCV, 2024

    2024

  20. [28]

    Loopy: Taming audio-driven portrait avatar with long-term motion dependency,

    J. Jiang, C. Liang, J. Yang, G. Lin, T. Zhong, and Y . Zheng, “Loopy: Taming audio-driven portrait avatar with long-term motion dependency,” inICLR, 2025

    2025

  21. [29]

    AniPortrait: Audio-driven synthesis of photorealistic portrait animation,

    H. Wei, Z. Yang, and Z. Wang, “AniPortrait: Audio-driven synthesis of photorealistic portrait animation,” arXiv preprint arXiv:2403.17694, 2024

    2024 arXiv

  22. [31]

    CyberHost: A one-stage diffusion framework for audio-driven talking body generation,

    G. Lin, J. Jiang, C. Liang, T. Zhong, J. Yang, and Y . Zheng, “CyberHost: A one-stage diffusion framework for audio-driven talking body generation,” inICLR, 2025

    2025

  23. [32]

    OmniHuman-1: Rethinking the scaling-up of one-stage conditioned human animation models,

    G. Lin, J. Jiang, J. Yang, Z. Zheng, and C. Liang, “OmniHuman-1: Rethinking the scaling-up of one-stage conditioned human animation models,” arXiv preprint arXiv:2502.01061, 2025

    2025 arXiv

  24. [33]

    Dance any beat: Blending beats with visuals in dance video generation,

    W. Xuanchen, W. Heng, L. Dongnan, and W. Cai, “Dance any beat: Blending beats with visuals in dance video generation,” in WACV, 2025

    2025

  25. [34]

    X- dancer: Expressive music to human dance video generation,

    Z. Chen, H. Xu, G. Song, Y . Xie, C. Zhang, X. Chen, C. Wang, D. Chang, and L. Luo, “X- dancer: Expressive music to human dance video generation,” arXiv preprint arXiv:2502.17414, 2025

    2025 arXiv

  26. [35]

    Taming transformers for high-resolution image synthesis,

    P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in CVPR, 2021

    2021

  27. [36]

    A style-based generator architecture for generative adversarial networks,

    T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in CVPR, 2019

    2019

  28. [37]

    Träumerai: Dreaming music with stylegan,

    D. Jeong, S. Doh, and T. Kwon, “Träumerai: Dreaming music with stylegan,”arXiv preprint arXiv:2102.04680, 2021

    2021 arXiv

  29. [38]

    Ucf101: A dataset of 101 human actions classes from videos in the wild,

    K. Soomro, A. R. Zamir, and M. Shah, “Ucf101: A dataset of 101 human actions classes from videos in the wild,” arXiv preprint arXiv:1212.0402, 2012

    2012 arXiv

  30. [39]

    Content-based video-music retrieval using soft intra-modal structure constraint,

    S. Hong, W. Im, and H. S. Yang, “Content-based video-music retrieval using soft intra-modal structure constraint,” arXiv preprint arXiv:1704.06761, 2017. 13

    2017 arXiv

  31. [40]

    AudioSet: An ontology and human-labeled dataset for audio events,

    J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “AudioSet: An ontology and human-labeled dataset for audio events,” in ICASSP, 2017

    2017

  32. [41]

    V oxCeleb2: Deep speaker recognition,

    J. S. Chung, A. Nagrani, and A. Zisserman, “V oxCeleb2: Deep speaker recognition,” arXiv preprint arXiv:1806.05622, 2018

    2018 arXiv

  33. [42]

    VggSound: A large-scale audio-visual dataset,

    H. Chen, W. Xie, A. Vedaldi, and A. Zisserman, “VggSound: A large-scale audio-visual dataset,” in ICASSP, 2020

    2020

  34. [43]

    Frozen in time: A joint video and image encoder for end-to-end retrieval,

    M. Bain, A. Nagrani, G. Varol, and A. Zisserman, “Frozen in time: A joint video and image encoder for end-to-end retrieval,” in ICCV, 2021

    2021

  35. [44]

    InternVid: A large-scale video-text dataset for multimodal understanding and generation,

    Y . Wang, Y . He, Y . Li, K. Li, J. Yu, X. Ma, X. Li, G. Chen, X. Chen, Y . Wang,et al., “InternVid: A large-scale video-text dataset for multimodal understanding and generation,” in ICLR, 2024

    2024

  36. [45]

    CelebV-HQ: A large-scale video facial attributes dataset,

    H. Zhu, W. Wu, W. Zhu, L. Jiang, S. Tang, L. Zhang, Z. Liu, and C. C. Loy, “CelebV-HQ: A large-scale video facial attributes dataset,” in ECCV, 2022

    2022

  37. [46]

    MovieBench: A hierarchical movie level dataset for long video generation,

    W. Wu, M. Liu, Z. Zhu, X. Xia, H. Feng, W. Wang, K. Q. Lin, C. Shen, and M. Z. Shou, “MovieBench: A hierarchical movie level dataset for long video generation,” arXiv preprint arXiv:2411.15262, 2024

    2024 arXiv

  38. [47]

    Condensed movies: Story based retrieval with contextual embeddings,

    M. Bain, A. Nagrani, A. Brown, and A. Zisserman, “Condensed movies: Story based retrieval with contextual embeddings,” in ACCV, 2020

    2020

  39. [48]

    Short film dataset (SFD): A benchmark for story-level video understanding,

    R. Ghermi, X. Wang, V . Kalogeiton, and I. Laptev, “Short film dataset (SFD): A benchmark for story-level video understanding,” arXiv preprint arXiv:2406.10221, 2024

    2024 arXiv

  40. [49]

    VideoCrafter2: Over- coming data limitations for high-quality video diffusion models,

    H. Chen, Y . Zhang, X. Cun, M. Xia, X. Wang, C. Weng, and Y . Shan, “VideoCrafter2: Over- coming data limitations for high-quality video diffusion models,” in CVPR, 2024

    2024

  41. [50]

    Video cut detection and analysis tool

    B. Castellano, “Video cut detection and analysis tool.” https://github.com/Breakthrough/PySceneDetect

  42. [51]

    Meta audiobox aesthetics: Unified automatic quality assessment for speech, music, and sound,

    A. Tjandra, Y .-C. Wu, B. Guo, J. Hoffman, B. Ellis, A. Vyas, B. Shi, S. Chen, M. Le, N. Zacharov, et al., “Meta audiobox aesthetics: Unified automatic quality assessment for speech, music, and sound,” arXiv preprint arXiv:2502.05139, 2025

    2025 arXiv

  43. [52]

    Video instruction tuning with synthetic data,

    Y . Zhang, J. Wu, W. Li, B. Li, Z. Ma, Z. Liu, and C. Li, “Video instruction tuning with synthetic data,” arXiv preprint arXiv:2410.02713, 2024

    2024 arXiv

  44. [53]

    Cinematic sound demixing

    R. Solovyev, “Cinematic sound demixing.” https://github.com/ZFTurbo/MVSEP-CDX23- Cinematic-Sound-Demixing

  45. [54]

    Spleeter: a fast and efficient music source separation tool with pre-trained models,

    R. Hennequin, A. Khlif, F. V oituret, and M. Moussallam, “Spleeter: a fast and efficient music source separation tool with pre-trained models,” Journal of Open Source Software, 2020

    2020

  46. [55]

    Ultralytics YOLO

    G. Jocher, J. Qiu, and A. Chaurasia, “Ultralytics YOLO.” https://github.com/ultralytics/ultralytics

  47. [56]

    Meet scribe

    Elevenlabs, “Meet scribe.” https://elevenlabs.io/blog/meet-scribe

  48. [57]

    TalkNet 2: Non-autoregressive depth-wise separable convolu- tional model for speech synthesis with explicit pitch and duration prediction,

    S. Beliaev and B. Ginsburg, “TalkNet 2: Non-autoregressive depth-wise separable convolu- tional model for speech synthesis with explicit pitch and duration prediction,”arXiv preprint arXiv:2104.08189, 2021

    2021 arXiv

  49. [58]

    wav2vec 2.0: a framework for self-supervised learning of speech representations,

    A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: a framework for self-supervised learning of speech representations,” in NeurIPS, 2020

    2020

  50. [59]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  51. [60]

    To- wards accurate generative models of video: A new metric & challenges,

    T. Unterthiner, S. Van Steenkiste, K. Kurach, R. Marinier, M. Michalski, and S. Gelly, “To- wards accurate generative models of video: A new metric & challenges,” arXiv preprint arXiv:1812.01717, 2018

    2018 arXiv

  52. [61]

    Learning transferable visual models from natural language supervi- sion,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al., “Learning transferable visual models from natural language supervi- sion,” in ICML, 2021

    2021

  53. [62]

    VideoCLIP-XL: Advancing long description understanding for video clip models,

    J. Wang, C. Wang, K. Huang, J. Huang, and L. Jin, “VideoCLIP-XL: Advancing long description understanding for video clip models,” arXiv preprint arXiv:2410.00741, 2024. 14

    2024 arXiv

  54. [63]

    ImageBind: One embedding space to bind them all,

    R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V . Alwala, A. Joulin, and I. Misra, “ImageBind: One embedding space to bind them all,” in CVPR, 2023

    2023

  55. [64]

    Out of time: automated lip sync in the wild,

    J. S. Chung and A. Zisserman, “Out of time: automated lip sync in the wild,” in ACCV Workshops, 2017

    2017

  56. [65]

    DitCtrl: Exploring attention control in multi-modal diffusion transformer for tuning-free multi-prompt longer video generation,

    M. Cai, X. Cun, X. Li, W. Liu, Z. Zhang, Y . Zhang, Y . Shan, and X. Yue, “DitCtrl: Exploring attention control in multi-modal diffusion transformer for tuning-free multi-prompt longer video generation,” arXiv preprint arXiv:2412.18597, 2024

    2024 arXiv

  57. [66]

    TA VGBench: Benchmarking text to audible-video generation,

    Y . Mao, X. Shen, J. Zhang, Z. Qin, J. Zhou, M. Xiang, Y . Zhong, and Y . Dai, “TA VGBench: Benchmarking text to audible-video generation,” in ACM Multimedia, 2024

    2024

  58. [67]

    MMDisco: Multi-modal discriminator- guided cooperative diffusion for joint audio and video generation,

    A. Hayakawa, M. Ishii, T. Shibuya, and Y . Mitsufuji, “MMDisco: Multi-modal discriminator- guided cooperative diffusion for joint audio and video generation,” inICLR, 2025

    2025

  59. [68]

    A V-Dit: Efficient audio-visual diffusion transformer for joint audio and video generation,

    K. Wang, S. Deng, J. Shi, D. Hatzinakos, and Y . Tian, “A V-Dit: Efficient audio-visual diffusion transformer for joint audio and video generation,” arXiv preprint arXiv:2406.07686, 2024

    2024 arXiv

  60. [69]

    Hallo2: Long-duration and high-resolution audio-driven portrait image animation,

    J. Cui, H. Li, Y . Yao, H. Zhu, H. Shang, K. Cheng, H. Zhou, S. Zhu, and J. Wang, “Hallo2: Long-duration and high-resolution audio-driven portrait image animation,” in ICLR, 2025

    2025

  61. [70]

    SadTalker: Learning realistic 3d motion coefficients for stylized audio-driven single image talking face animation,

    W. Zhang, X. Cun, X. Wang, Y . Zhang, X. Shen, Y . Guo, Y . Shan, and F. Wang, “SadTalker: Learning realistic 3d motion coefficients for stylized audio-driven single image talking face animation,” in CVPR, 2023

    2023

  62. [71]

    Maximum filter vibrato suppression for onset detection,

    S. Böck and G. Widmer, “Maximum filter vibrato suppression for onset detection,” Citeseer

  63. [72]

    Determining optical flow,

    B. K. Horn and B. G. Schunck, “Determining optical flow,” Artificial intelligence, 1981. 15

    1981

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.