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REVIEW 4 major objections 5 minor 57 references

Controllable Video-to-Music Generation with Multiple Time-Varying Conditions

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper builds a video-to-music generator whose output can be steered by four time-varying controls—beat, melody, intensity, and emotion—on top of the video itself.

desk verdict Timely and architecturally sensible, but the evaluation tables contradict each other so severely that the paper's central claims are unsupported. read the letter →

arxiv 2507.20627 v1 pith:45HBOASO submitted 2025-07-28 cs.MM cs.AIcs.SDeess.AS

Junxian Wu , Weitao You , Heda Zuo , Dengming Zhang , Pei Chen , Lingyun Sun This is my paper
classification cs.MMcs.AIcs.SDeess.AS
keywords video-to-musicgenerationcontrollablemusicmulti-conditioncontroltime-varyingcontrolstemporalalignmenttwo-stagetrainingdynamicconditionalfusionmusic-videocorrespondence
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

Generating music from a video is usually a black box: the model sees frames and emits a soundtrack, but the user cannot ask for a calmer passage, a different melody, or a stronger beat. This paper tries to establish that those requests can be honored by conditioning generation on four time-varying signals—rhythm, intensity, melody, and emotion—alongside the video. The proposed two-stage training first teaches the model to align music with video, then adds lightweight modules that let the four conditions steer a frozen, already-trained generation backbone. The paper reports that this design outperforms existing video-to-music systems on both objective metrics and listening tests, and that users can apply any subset of the four controls, including asking for control over only part of a music segment.

What carries the argument

The load-bearing object is a decoder-only Transformer built from 4D-Blocks, each containing four cascaded masked self-attention and masked cross-attention layers. Together with a video feature aggregation head and a fine-grained feature selection module, its progressive temporal alignment attention gives music tokens an adaptive temporal receptive field over video frames, starting global and narrowing to 5, 3, and 2 seconds across layers. On top of this frozen backbone, the dynamic conditional fusion module partitions the four time-varying control signals into temporal patches and learns per-patch weights with inter- and intra-patch convolutions, and the control-guided decoder injects the fused conditions through a trainable copy of the first block layer connected by zero-initialized linear layers. These pieces are what the paper argues let a fixed generator absorb beat, melody, intensity, and emotion controls without retraining the whole model.

What would settle it

Recompute the full model's KLD and FAD on one fixed test set and check whether the values 0.84, 2.19 and 0.10, 4.47 can both be reproduced; if they cannot, the main and ablation results are measuring different benchmarks.

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Extended reading notes

Core claim

The paper's central claim is that controllability in video-to-music generation does not require building a new generator from scratch. Its key move is to train a video-conditioned music decoder in two stages: first it learns fine-grained visual feature selection and progressive temporal alignment, so that music tokens attend to video frames in windows that shrink from global to five, three, and two seconds; then it freezes that backbone and attaches small trainable branches. A dynamic conditional fusion module weights the four time-varying controls per time step, and a control-guided decoder injects the fused signal into a trainable copy of the first attention layer of each block through zero-initialized linear layers. On the paper's own account, the result is music that follows user-specified dynamics while remaining synchronized with the video, and the reported evaluations claim the method beats prior V2M pipelines on fidelity, diversity, music-video correspondence, and condition-following metrics such as rhythm F1 and emotion CCC.

Load-bearing premise

The comparisons assume every table reports results on the same randomly sampled evaluation set; if the ablation runs used a different subset, the claimed margins over baselines are not directly comparable.

Editorial extensions

If this is right

  • A user can steer one musical dimension, say intensity, while leaving rhythm, melody, and emotion to the model, because each control is injected separately and can be dropped during training.
  • The model can follow partial control curves: it will restore a deliberately omitted segment of a condition, for instance the middle of a melody control, with musically consistent content.
  • Because the generation backbone is frozen during fine-tuning, adding a new control signal costs only the small conditional branch and a short fine-tune, not a full retrain.
  • Evaluations of V2M systems would need to include controllability metrics such as rhythm F1, melody accuracy, and emotion and intensity PCC/CCC alongside music fidelity and video correspondence, which is what the paper does in its multi-condition tables.

Reading between the lines

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

  • Editorial inference: the paper's created-controls experiments point toward a co-creation workflow in which a user draws an intensity curve or picks a rhythm preset and the model fills in the rest; the main paper only sketches this, but it is the natural product use.
  • Editorial inference: because the conditioning branches are small and the backbone is frozen, the same recipe could plausibly bolt time-varying controls onto other pretrained music generators, not just this video-conditioned one.
  • The paper itself notes in its limitation discussion that when many conditions are given at once they can conflict with one another and with the video style, so the practical ceiling on controllability is set by how well the model resolves those conflicts rather than by how many knobs it offers.
  • Editorial inference: the full model's KLD and FAD differ sharply between the main table (0.84 and 2.19) and the ablation table (0.10 and 4.47), so reproducing both on one fixed evaluation set would clarify how much of the reported gain is due to the modules rather than to test-set selection.
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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 / 5 minor

Summary. The paper proposes a two-stage video-to-music (V2M) generation framework with multiple time-varying conditions: rhythm, intensity, melody, and emotion. In stage one, a video feature aggregation module, a fine-grained feature selection module, and a progressive temporal alignment attention mechanism provide video-conditioned music generation. In stage two, a dynamic conditional fusion module and a control-guided decoder integrate user-specified conditions while freezing the pretrained backbone. The authors claim state-of-the-art objective and subjective results over several V2M baselines, including versions of GVMGen and VidMuse augmented with their two-stage modules. The main technical contributions are the two-stage training scheme, the PTAA multi-window temporal alignment, and the DCF/CGD conditioning pathway.

Significance. If the reported results were reliable, the work would be a meaningful step toward controllable V2M generation: it addresses a genuine gap (time-varying multi-condition control in V2M, not just text or audio conditions) and the modular design is coherent. The created-controls evaluation in the supplementary (Appendix D.3) is a thoughtful attempt to test generalization beyond extracted controls. However, the empirical claims are currently undermined by severe inconsistencies in the reported numbers for the same model across tables, and by a circular evaluation of emotion controllability. The framework may be publishable after a careful re-evaluation, but the current evidence does not support the abstract's claim of outperforming prior pipelines.

major comments (4)
  1. [Tables 1, 2, 4, 5] The full model 'Ours' is reported with KLD 0.84 in Table 1, 0.96 in Table 2, and 0.10 in both Tables 4 and 5, while FAD varies from 2.19 to 4.47 for the same configuration. These differences are far too large to be attributed to metric noise and indicate a change in evaluation subset, feature extractor, decoding hyperparameters, or random seed that is not disclosed. The paper's central claim that 'Ours' outperforms all baselines relies on cross-table comparisons that are not interpretable under this inconsistency. The authors must fix a single evaluation set with a disclosed seed, report all runs on that set, and either explain the remaining discrepancies or adjust the claims.
  2. [Section 3.4 and Section 4.3] Emotion controls are extracted with the authors' prior dynamic music emotion recognition model [50], and the same model is used to compute the PCC/CCC emotion controllability metrics. This creates a circular loop: the generator only needs to match the extractor's predictions rather than the true emotional content, so the high emotion-control scores in Table 2 may reflect overfitting to that specific extractor. The created-controls experiments in Appendix D.3, which use hand-designed curves, show much lower controllability for the full model (Table 9, e.g., emotion arousal CCC 0.12 vs 0.67 in Table 2), which is consistent with this concern. An independent emotion model, human annotations, or a clear justification for why the same extractor is not circular should be provided.
  3. [Section 4.1] The evaluation set is described only as 'randomly sample and combine portions from V2M-bench [42], SymMV [55], and GVMGen test set' with no sample sizes, proportions, or random seed, and the supplementary does not add these details. Without a fixed, released evaluation split, the score inconsistencies between tables cannot be resolved, and the results are not reproducible. The authors should specify the exact number of videos from each source, the sampling procedure, and ideally release the list of video IDs or an official test split.
  4. [Table 4] Removing the CGD module yields intensity PCC/CCC of 0.83/0.71, which is higher than the full model's 0.71/0.55. The text acknowledges this in Section 4.7, but the abstract and conclusion claim that CGD and the other modules 'accurately guide' and improve controllability. This intensity-specific trade-off should be treated as a limitation rather than an incidental note, and the paper should either provide a metric that aggregates controllability across all four conditions or soften the claim that every component is beneficial.
minor comments (5)
  1. [Eq. (5)] The variable T in the definition γ∈{0,1,...,T/k_j} is not defined; it should presumably be T_m (the music token length), and the mask condition should clarify that γ = floor(i/k_j). Please correct this notation.
  2. [Section 4.2] The condition dropout rate is stated as 0.5 with p=0.05 and q=0.05 in Eq. (12), but the semantics of the 0.5 rate versus p and q is not explained. Please clarify how the dropout applies in training.
  3. [Eq. (10)] The phrase 'F(· ; Θ) represents a trained neutral block' should read 'neural block' rather than 'neutral block'; this appears to be a typo.
  4. [Figures 3 and 4] The architecture diagrams are dense and contain many abbreviations (e.g., 'B×Tv×P×D', 'Mask SA+CAM5') that are hard to read at printed size. Please enlarge fonts and consider a legend that explains the tensor shapes and mask naming.
  5. [Appendix D.3 and Table 9] The created-controls results show a large drop in controllability relative to extracted controls (e.g., rhythm F1 42.2% vs 81.9% in Table 2). This suggests the model may not generalize to user-designed controls, which is a core use case claimed in the paper. Please discuss this discrepancy explicitly.

Circularity Check

1 steps flagged · score 6.0 of 10

Emotion controllability is evaluated with the same self-cited dynamic emotion extractor that generates the emotion condition, closing the loop; the architectural derivation and other control modalities remain non-circular.

  1. self definitional [Section 3.4 (Emotion control signal) and Section 4.3 (Objective Evaluation Metrics), with results in Tables 2 and 4]
    "We use a dynamic music emotion recognition model [50] to extract the dynamic V and A values CEmo of the music ... For emotion and intensity evaluation, we utilize Pearson Correlation Coefficient (PCC) and Concordance Correlation Coefficient (CCC) to quantify the relationship between the frame-level values of the input and those derived from the generated output, following [47, 50]."

    The valence/arousal condition fed to the generator is defined as the frame-level output of the authors' own dynamic music emotion recognition model [50]. The PCC/CCC emotion-controllability metric is then computed between that input condition and the valence/arousal values 'derived from the generated output', with the paper citing the same [50] protocol as the basis. Since no other emotion extractor is identified in the paper, the generated music is effectively scored by the same function that produced the input condition. A model can therefore obtain high reported emotion-control scores by matching the statistical preferences of this one extractor, without independent evidence that human listeners perceive the intended emotion.

full rationale

I walked the paper's derivation chain looking for reductions of claimed results to the paper's own inputs. The architectural claims — two-stage pre-training/fine-tuning, Fine-Grained Feature Selection, Progressive Temporal Alignment Attention, Dynamic Conditional Fusion, Control-Guided Decoder, and the masking strategy — are implemented and ablated as stated; no fitted parameter is relabeled as a prediction, and no uniqueness or ansatz is imported solely from the authors' prior work. The melody, rhythm, and intensity controls are extracted with standard signal-processing tools (Madmom, Librosa, Savitzky-Golay, MIR_EVAL), and comparing the same style of extraction on output audio is a conventional controllability protocol rather than a construction-level circularity. The one genuine circular step is the emotion channel: the emotion condition is produced by the authors' own dynamic emotion recognition model [50], and the same model family is used, via the 'following [47, 50]' evaluation protocol, to score the emotion of the generated audio. Consequently, the reported emotion PCC/CCC values measure how well the generator reproduces the statistics that this specific self-cited extractor responds to, not an independently validated emotional alignment. This is a partial circularity affecting the emotion-controllability claim; the rest of the paper's evaluation, including subjective UEC and the non-emotion controls, gives independent support. The large cross-table inconsistencies in the full model's KLD/FAD (e.g., 0.84/2.19 in Table 1 versus 0.10/4.18 in Tables 4 and 5) are a serious evaluation-consistency and reproducibility concern, but they are not a circular-derivation step and therefore do not further raise the circularity score under the stated rules.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim depends on the adequacy of pretrained feature extractors (CLIP, VideoMAE, Encodec), on the accuracy of the authors' own emotion recognition model used both as condition and evaluator, and on a stable, representative evaluation set. The evaluation instability observed across tables means the last assumption is not met.

free parameters (4)
  • PTAA mask window sizes k_j = infinity, 5, 3, 2 seconds
    Hand-chosen hierarchical receptive fields for temporal alignment; not derived from data.
  • DCF patch size P = 50
    Hand-chosen patch length for temporal-conditioned fusion.
  • Condition dropout rates = overall 0.5, p=0.05, q=0.05
    Hyperparameters for masking partial music controls; set manually.
  • Number of 4D-Blocks and feature dimension = 12 blocks, 1536 dim
    Architecture choices for the decoder backbone, inherited from MusicGen-style designs.
assumptions (6)
  • standard math Attention mechanisms and softmax are differentiable and suitable for conditional generation.
    Used throughout the model in Equations 3 through 5.
  • domain assumption CLIP, VideoMAE V2, and Encodec features faithfully represent video and audio content for V2M.
    These pretrained features are the sole input representation; no analysis of failure cases is provided.
  • domain assumption The dynamic music emotion recognition model of Zhang et al. [50] accurately estimates valence and arousal.
    Used both to create the emotion condition and to evaluate emotion controllability, creating a circular loop.
  • domain assumption The GVMGen dataset is representative and high-quality for training V2M models.
    The model is trained entirely on this dataset, and no independent data is used for training.
  • domain assumption The evaluation metrics (KLD, FAD, etc.) discriminate meaningful differences in music quality and correspondence.
    The paper relies on these metrics to support the outperformance claim.
  • domain assumption The randomly sampled test set is fixed and consistent across all experiments.
    The inconsistent results across tables suggest this assumption is not met.

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Cite this review

Pith. "Pith review of Controllable Video-to-Music Generation with Multiple Time-Varying Conditions." pith.science (2026). https://pith.science/paper/45HBOASO

@misc{pith2026250720627,
  author       = {Pith},
  title        = {Pith review of: Controllable Video-to-Music Generation with Multiple Time-Varying Conditions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/45HBOASO}},
  note         = {Machine review of arXiv:2507.20627}
}
read the original abstract

Music enhances video narratives and emotions, driving demand for automatic video-to-music (V2M) generation. However, existing V2M methods relying solely on visual features or supplementary textual inputs generate music in a black-box manner, often failing to meet user expectations. To address this challenge, we propose a novel multi-condition guided V2M generation framework that incorporates multiple time-varying conditions for enhanced control over music generation. Our method uses a two-stage training strategy that enables learning of V2M fundamentals and audiovisual temporal synchronization while meeting users' needs for multi-condition control. In the first stage, we introduce a fine-grained feature selection module and a progressive temporal alignment attention mechanism to ensure flexible feature alignment. For the second stage, we develop a dynamic conditional fusion module and a control-guided decoder module to integrate multiple conditions and accurately guide the music composition process. Extensive experiments demonstrate that our method outperforms existing V2M pipelines in both subjective and objective evaluations, significantly enhancing control and alignment with user expectations.

Figures

Figures reproduced from arXiv: 2507.20627 by the authors.

Figure 1
Figure 1. Comparison of V2M generation frameworks be [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The main architecture of the proposed model, consisting of a two-stage process: pre-training and fine-tuning. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Video-to-music generation pre-training stage. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Multi-condition control fine-tuning stage. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Spectrograms of the music generated by each model based on four types of video inputs. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Examples of music generated by each model, given single time-varying control and input video. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: An example of music generation guided by input video and multiple user-specified time-varying conditions. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: An example of music generation guided by video input and multiple time-varying conditions that are partially [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

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Works this paper leans on

57 extracted references · 39 canonical work pages

  1. [50]

    Dengming Zhang, Weitao You, Ziheng Liu, Lingyun Sun, and Pei Chen. 2025. Per- sonalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 39. 1629–1637

    work page 2025

  2. [42]

    Zeyue Tian, Zhaoyang Liu, Ruibin Yuan, Jiahao Pan, Xiaoqiang Huang, Qifeng Liu, Xu Tan, Qifeng Chen, Wei Xue, and Yike Guo. 2024. VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling. arXiv preprint arXiv:2406.04321 (2024)

    arXiv 2024

  3. [55]

    Le Zhuo, Zhaokai Wang, Baisen Wang, Yue Liao, Chenxi Bao, Stanley Peng, Songhao Han, Aixi Zhang, Fei Fang, and Si Liu. 2023. Video background music generation: Dataset, method and evaluation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 15637–15647

    work page 2023

  4. [1]

    Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. 2016. Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675 (2016)

    arXiv 2016

  5. [2]

    Andrea Agostinelli, Timo I Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, An- toine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al. 2023. Musiclm: Generating music from text. arXiv preprint arXiv:2301.11325 (2023)

    arXiv 2023

  6. [3]

    Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, and Gerhard Widmer. 2016. Madmom: A new python audio and music signal processing library. In Proceedings of the 24th ACM international conference on Multimedia . 1174–1178

    work page 2016

  7. [4]

    Sebastian Böck, Florian Krebs, and Gerhard Widmer. 2016. Joint Beat and Down- beat Tracking with Recurrent Neural Networks.. In ISMIR. New York City, 255– 261

    work page 2016

  8. [5]

    Jongho Choi and Kyogu Lee. 2023. Pop2Piano: Pop audio-based piano cover generation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 1–5

    work page 2023

Show all 57 references
  1. [6]

    Sanjoy Chowdhury, Sayan Nag, KJ Joseph, Balaji Vasan Srinivasan, and Dinesh Manocha. 2024. MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 26826–26835

    2024

  2. [7]

    Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, and Alexandre Défossez. 2024. Simple and controllable music generation. Advances in Neural Information Processing Systems 36 (2024)

    2024

  3. [8]

    Aurora Linh Cramer, Ho-Hsiang Wu, Justin Salamon, and Juan Pablo Bello. 2019. Look, listen, and learn more: Design choices for deep audio embeddings. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3852–3856

    2019

  4. [9]

    Johanna N Dasovich-Wilson, Marc Thompson, and Suvi Saarikallio. 2022. Ex- ploring music video experiences and their influence on music perception. Music & Science 5 (2022), 20592043221117651

    2022

  5. [10]

    Matthew EP Davies, Norberto Degara, and Mark D Plumbley. 2009. Evaluation methods for musical audio beat tracking algorithms. Queen Mary University of London, Centre for Digital Music, Tech. Rep. C4DM-TR-09-06 (2009)

    2009

  6. [11]

    Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi. 2022. High fidelity neural audio compression. arXiv preprint arXiv:2210.13438 (2022)

    2022 arXiv

  7. [12]

    Shangzhe Di, Zeren Jiang, Si Liu, Zhaokai Wang, Leyan Zhu, Zexin He, Hong- ming Liu, and Shuicheng Yan. 2021. Video background music generation with controllable music transformer. In Proceedings of the 29th ACM International Conference on Multimedia. 2037–2045

    2021

  8. [13]

    Zach Evans, CJ Carr, Josiah Taylor, Scott H Hawley, and Jordi Pons. 2024. Fast timing-conditioned latent audio diffusion. In Proceedings of the 41st International Conference on Machine Learning . 12652–12665

    2024

  9. [14]

    Chuang Gan, Deng Huang, Peihao Chen, Joshua B Tenenbaum, and Antonio Torralba. 2020. Foley music: Learning to generate music from videos. InComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 . Springer, 758–775

    2020

  10. [15]

    Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. 2017. Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal proces...

    2017

  11. [16]

    Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. 2023. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 15180–15190

    2023

  12. [17]

    Azalea Gui, Hannes Gamper, Sebastian Braun, and Dimitra Emmanouilidou. 2024. Adapting frechet audio distance for generative music evaluation. In ICASSP 2024- 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1331–1335

    2024

  13. [18]

    Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Channing Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et al. 2017. CNN architectures for large-scale audio classification. In 2017 ieee international conference on acoustics, s...

    2017

  14. [19]

    Shulei Ji, Songruoyao Wu, Zihao Wang, Shuyu Li, and Kejun Zhang. 2025. A Comprehensive Survey on Generative AI for Video-to-Music Generation. arXiv preprint arXiv:2502.12489 (2025)

    2025

  15. [20]

    Jaeyong Kang, Soujanya Poria, and Dorien Herremans. 2024. Video2music: Suitable music generation from videos using an affective multimodal transformer model. Expert Systems with Applications 249 (2024), 123640

    2024

  16. [21]

    Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, and Matthew Sharifi. 2018. Fr\’echet audio distance: A metric for evaluating music enhancement algorithms. arXiv preprint arXiv:1812.08466 (2018)

    2018 arXiv

  17. [22]

    Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley. 2020. Panns: Large-scale pretrained audio neural networks for audio pattern recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020), 2880–2894

    2020

  18. [23]

    Florian Krebs, Sebastian Böck, and Gerhard Widmer. 2015. An Efficient State- Space Model for Joint Tempo and Meter Tracking.. In ISMIR. 72–78

    2015

  19. [24]

    Yun-Han Lan, Wen-Yi Hsiao, Hao-Chung Cheng, and Yi-Hsuan Yang. 2024. Mu- sicongen: Rhythm and chord control for transformer-based text-to-music gener- ation. arXiv preprint arXiv:2407.15060 (2024)

    2024 arXiv

  20. [25]

    Chenghan Li, Mingchen Li, and Ruisheng Diao. 2025. TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation. arXiv preprint arXiv:2503.07674 (2025)

    2025 arXiv

  21. [26]

    Ruiqi Li, Siqi Zheng, Xize Cheng, Ziang Zhang, Shengpeng Ji, and Zhou Zhao

  22. [27]

    Sizhe Li, Yiming Qin, Minghang Zheng, Xin Jin, and Yang Liu. 2024. Diff-BGM: A Diffusion Model for Video Background Music Generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 27348–27357

    2024

  23. [28]

    Sifei Li, Binxin Yang, Chunji Yin, Chong Sun, Yuxin Zhang, Weiming Dong, and Chen Li. 2024. VidMusician: Video-to-Music Generation with Semantic-Rhythmic Alignment via Hierarchical Visual Features. arXiv preprint arXiv:2412.06296 (2024)

    2024 arXiv

  24. [29]

    Qihao Liang and Ye Wang. 2024. Drawlody: Sketch-Based Melody Creation with Enhanced Usability and Interpretability. IEEE Transactions on Multimedia (2024)

    2024

  25. [30]

    Yan-Bo Lin, Yu Tian, Linjie Yang, Gedas Bertasius, and Heng Wang. 2024. VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos. arXiv preprint arXiv:2409.07450 (2024)

    2024 arXiv

  26. [31]

    Shansong Liu, Atin Sakkeer Hussain, Chenshuo Sun, and Ying Shan. 2023. M2UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models. arXiv preprint arXiv:2311.11255 (2023)

    2023 arXiv

  27. [32]

    Lin Ma. 2022. Research on the effect of different types of short music videos on viewers’ psychological emotions. Frontiers in Public Health 10 (2022), 992200

    2022

  28. [33]

    Jan Melechovsky, Zixun Guo, Deepanway Ghosal, Navonil Majumder, Dorien Herremans, and Soujanya Poria. 2023. Mustango: Toward controllable text-to- music generation. arXiv preprint arXiv:2311.08355 (2023)

    2023 arXiv

  29. [34]

    Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo. 2020. Reliable fidelity and diversity metrics for generative models. In International conference on machine learning . PMLR, 7176–7185

    2020

  30. [35]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learni...

    2021

  31. [36]

    Colin Raffel, Brian McFee, Eric J Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, Daniel PW Ellis, and C Colin Raffel. 2014. MIR_EVAL: A Transparent Implementation of Common MIR Metrics.. In ISMIR, Vol. 10. 2014

    2014

  32. [37]

    Simon Rouard, Yossi Adi, Jade Copet, Axel Roebel, and Alexandre Défossez. 2024. Audio Conditioning for Music Generation via Discrete Bottleneck Features.arXiv preprint arXiv:2407.12563 (2024)

    2024 arXiv

  33. [38]

    Simon Rouard, Francisco Massa, and Alexandre Défossez. 2023. Hybrid trans- formers for music source separation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 1–5

    2023

  34. [39]

    James A Russell. 1980. A circumplex model of affect. Journal of personality and social psychology 39, 6 (1980), 1161

    1980

  35. [40]

    Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, et al. 2024. V2Meow: Meowing to the Visual Beat via Video-to-Music Generation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol...

    2024

  36. [41]

    Kun Su, Xiulong Liu, and Eli Shlizerman. 2020. Multi-instrumentalist net: Unsupervised generation of music from body movements. arXiv preprint arXiv:2012.03478 (2020)

    2020 arXiv

  37. [43]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)

    2017

  38. [44]

    Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al . 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods 17, 3 (2020), 261–272

    2020

  39. [45]

    Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, and Yu Qiao. 2023. Videomae v2: Scaling video masked autoencoders with MM ’25, October 27–31, 2025, Dublin, Ireland Junxian Wu, Weitao You, Heda Zuo et al. dual masking. In Proceedings of the IEEE/...

    2023

  40. [46]

    Yajie Wang, Mulin Chen, and Xuelong Li. 2023. Continuous Emotion-Based Image-to-Music Generation. IEEE Transactions on Multimedia (2023)

    2023

  41. [47]

    Shih-Lun Wu, Chris Donahue, Shinji Watanabe, and Nicholas J Bryan. 2024. Music controlnet: Multiple time-varying controls for music generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing 32 (2024), 2692–2703

    2024

  42. [48]

    Shih-Lun Wu and Yi-Hsuan Yang. 2023. MuseMorphose: Full-song and fine- grained piano music style transfer with one transformer VAE. IEEE/ACM Trans- actions on Audio, Speech, and Language Processing 31 (2023), 1953–1967

    2023

  43. [49]

    Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. 2023. Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech an...

    2023

  44. [51]

    Liqian Zhang and Magdalena Fuentes. 2025. SONIQUE: Video Background Music Generation Using Unpaired Audio-Visual Data. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1–5

    2025

  45. [52]

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding conditional con- trol to text-to-image diffusion models. InProceedings of the IEEE/CVF International Conference on Computer Vision . 3836–3847

    2023

  46. [53]

    Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan-Yee K Wong. 2023. Uni-controlnet: All-in-one control to text-to- image diffusion models. Advances in Neural Information Processing Systems 36 (2023), 11127–11150

    2023

  47. [54]

    Ye Zhu, Kyle Olszewski, Yu Wu, Panos Achlioptas, Menglei Chai, Yan Yan, and Sergey Tulyakov. 2022. Quantized gan for complex music generation from dance videos. In European Conference on Computer Vision . Springer, 182–199

    2022

  48. [56]

    rhythm presets

    Heda Zuo, Weitao You, Junxian Wu, Shihong Ren, Pei Chen, Mingxu Zhou, Yujia Lu, and Lingyun Sun. 2025. GVMGen: A General Video-to-Music Generation Model With Hierarchical Attentions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 23099–23107. Contro...

    2025

  49. [2024]

    arXiv preprint arXiv:2410.12957 (2024)

    MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization. arXiv preprint arXiv:2410.12957 (2024)

    2024 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.