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arxiv: 2509.22378 · v2 · submitted 2025-09-26 · 💻 cs.SD · cs.AI· cs.MM· eess.AS

Zero-Effort Image-to-Music Generation: An Interpretable RAG-based VLM Approach

Zijian Zhao , Dian Jin , Zijing Zhou This is my paper

Pith reviewed 2026-05-18 12:36 UTC · model grok-4.3

classification 💻 cs.SD cs.AIcs.MMeess.AS
keywords image-to-music generationvision-language modelsretrieval-augmented generationABC notationinterpretabilityzero-shot generationmulti-modal AImusic synthesis
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The pith

A vision-language model generates music from images using ABC notation and retrieval without any training.

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

This paper tries to establish that existing vision-language models can handle image-to-music generation in an interpretable and low-cost way. It converts images to music by first describing them in text, then using ABC notation as a shared language for both text and music. Multi-modal retrieval-augmented generation and self-refinement steps guide the model to produce better output without any fine-tuning or large training runs. The system also supplies text motivations and attention maps to explain why a piece of music was chosen for a given image. A sympathetic reader would care because prior approaches were either opaque black boxes or required heavy computation that most users cannot access.

Core claim

We propose the first Vision Language Model (VLM)-based I2M framework that offers high interpretability and low computational cost. Specifically, we utilize ABC notation to bridge the text and music modalities, enabling the VLM to generate music using natural language. We then apply multi-modal Retrieval-Augmented Generation (RAG) and self-refinement techniques to allow the VLM to produce high-quality music without external training. Furthermore, we leverage the generated motivations in text and the attention maps from the VLM to provide explanations for the generated results in both text and image modalities.

What carries the argument

ABC notation as a text bridge between image descriptions and music, combined with multi-modal RAG and self-refinement inside an off-the-shelf vision-language model.

If this is right

  • Image-to-music generation becomes practical for users who lack large datasets or GPU resources for training.
  • Outputs include explicit text explanations and visual attention maps, reducing the subjectivity problem in artistic mappings.
  • The method achieves higher music quality and image consistency than prior approaches according to both human studies and machine metrics.
  • Applications in gaming, advertising, and multi-modal art gain accessibility because the pipeline runs on standard VLMs with retrieval.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same zero-effort pattern could be tested on related tasks such as video-to-music or image-to-sound-effect generation by swapping the retrieval corpus.
  • Symbolic intermediaries like ABC notation may prove useful for adding controllability to other generative models that currently operate only in continuous audio spaces.
  • Performance would likely vary with the base VLM chosen, offering a clear experimental axis for measuring how pretraining data affects symbolic music output reliability.

Load-bearing premise

Existing vision-language models can reliably produce valid, high-quality music in ABC notation from image descriptions when guided by multi-modal RAG and self-refinement, without any task-specific training.

What would settle it

Running the system on a diverse set of images and finding that the output ABC notation is frequently invalid, produces low-quality audio, or shows no measurable improvement in human-rated consistency with the image would falsify the central claim.

Figures

Figures reproduced from arXiv: 2509.22378 by Dian Jin, Zijian Zhao, Zijing Zhou.

Figure 1
Figure 1. Figure 1: Overall Workflow of Proposed Framework TABLE I: Human Evaluation Results: The bold text indicates the best result, while underlined text represents the second-best result. This formatting will be consistent in the following tables. Methods Metrics Music Quality Music-Image Consistency Overall Melody Rhythm Authenticity Harmony Average Overall Semantics Emotion Average Synesthesia [6] 3.65 3.52 4.10 4.04 3.… view at source ↗
Figure 2
Figure 2. Figure 2: Generation Result using Input Image Shown in Fig. 1 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Recently, Image-to-Music (I2M) generation has garnered significant attention, with potential applications in fields such as gaming, advertising, and multi-modal art creation. However, due to the ambiguous and subjective nature of I2M tasks, most end-to-end methods lack interpretability, leaving users puzzled about the generation results. Even methods based on emotion mapping face controversy, as emotion represents only a singular aspect of art. Additionally, most learning-based methods require substantial computational resources and large datasets for training, hindering accessibility for common users. To address these challenges, we propose the first Vision Language Model (VLM)-based I2M framework that offers high interpretability and low computational cost. Specifically, we utilize ABC notation to bridge the text and music modalities, enabling the VLM to generate music using natural language. We then apply multi-modal Retrieval-Augmented Generation (RAG) and self-refinement techniques to allow the VLM to produce high-quality music without external training. Furthermore, we leverage the generated motivations in text and the attention maps from the VLM to provide explanations for the generated results in both text and image modalities. To validate our method, we conduct both human studies and machine evaluations, where our method outperforms others in terms of music quality and music-image consistency, indicating promising results. Our code is available at https://github.com/RS2002/Image2Music .

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes the first VLM-based Image-to-Music (I2M) framework that generates music from images in a zero-effort manner by using ABC notation as a bridge between modalities, combined with multi-modal RAG and self-refinement to avoid any task-specific training or fine-tuning. It claims high interpretability through generated textual motivations and VLM attention maps, low computational cost, and superior performance over prior methods in music quality and music-image consistency, validated via human studies and machine evaluations. The code is made publicly available.

Significance. If the performance and reliability claims hold after detailed validation, the work would offer a practical, accessible, and interpretable alternative to resource-heavy end-to-end I2M models, with potential applications in gaming, advertising, and multi-modal art. The open-source code is a clear strength that supports reproducibility. The dual-modality explanation mechanism could improve user trust in generative systems, though the central assumption that unmodified VLMs reliably output valid ABC notation requires stronger empirical grounding to realize this impact.

major comments (3)
  1. [Abstract] Abstract: The claim that 'our method outperforms others in terms of music quality and music-image consistency' is stated without any quantitative metrics, statistical tests, dataset sizes, or baseline details, leaving the central empirical claim with limited verifiable support.
  2. [Method] Method (core pipeline description): The zero-effort claim rests on the assumption that existing VLMs, guided only by multi-modal RAG and self-refinement, will reliably emit syntactically correct and musically coherent ABC notation; no explicit syntax validation, constraint enforcement, or failure-mode analysis for bar lines, durations, or key signatures is described, which is load-bearing given known VLM limitations on structured symbolic output.
  3. [Experiments] Experiments: The human studies and machine evaluations lack specification of participant numbers, exact metrics, evaluation protocols, or comparison methods, preventing assessment of whether the reported outperformance is robust or generalizable.
minor comments (2)
  1. The abstract states that code is available at a GitHub link, but the main text would benefit from a persistent identifier or explicit reproducibility checklist.
  2. Notation for the RAG retrieval and self-refinement loop could be clarified with a high-level algorithm box or pseudocode to improve readability for readers unfamiliar with the exact prompting strategy.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment point by point below, providing clarifications from the manuscript and proposing targeted revisions to improve transparency and rigor where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'our method outperforms others in terms of music quality and music-image consistency' is stated without any quantitative metrics, statistical tests, dataset sizes, or baseline details, leaving the central empirical claim with limited verifiable support.

    Authors: We agree that the abstract presents a high-level summary without embedding the specific quantitative results. The full manuscript reports these details in the Experiments section, including human preference rates, machine consistency scores, and comparisons against prior baselines on a defined image set. To strengthen the abstract's support for the claim while preserving its conciseness, we will revise it to include brief references to the key empirical outcomes (e.g., superior performance in human studies and objective metrics) and direct readers to the corresponding tables and statistical analyses. revision: yes

  2. Referee: [Method] Method (core pipeline description): The zero-effort claim rests on the assumption that existing VLMs, guided only by multi-modal RAG and self-refinement, will reliably emit syntactically correct and musically coherent ABC notation; no explicit syntax validation, constraint enforcement, or failure-mode analysis for bar lines, durations, or key signatures is described, which is load-bearing given known VLM limitations on structured symbolic output.

    Authors: This observation correctly identifies a central assumption. The self-refinement stage iteratively prompts the VLM to detect and correct syntactic and musical inconsistencies in the generated ABC notation, leveraging the model's own reasoning capabilities without external parsers. However, the initial submission does not include a dedicated failure-mode analysis or quantitative breakdown of syntax error rates before and after refinement. We will add a new subsection under the Method describing common ABC syntax issues (e.g., invalid bar lines or durations), the refinement prompt strategy for addressing them, and empirical correction statistics drawn from our test cases. This addition will provide stronger empirical grounding for the zero-effort approach. revision: yes

  3. Referee: [Experiments] Experiments: The human studies and machine evaluations lack specification of participant numbers, exact metrics, evaluation protocols, or comparison methods, preventing assessment of whether the reported outperformance is robust or generalizable.

    Authors: The manuscript does describe the human study protocol, participant recruitment, metrics (including quality and consistency ratings), and baseline comparisons in the Experiments section. That said, we acknowledge that the presentation could be more explicit regarding exact participant counts, statistical tests, and precise rating scales to facilitate reproducibility. We will expand this section with additional details on the evaluation protocol, including the number of participants, exact questionnaire items, inter-rater agreement measures, and the statistical methods used for significance testing. These clarifications will make the robustness and generalizability of the results easier to assess. revision: partial

Circularity Check

0 steps flagged

No circularity: standard VLM+RAG application to I2M with external evaluation

full rationale

The paper presents an engineering pipeline that applies off-the-shelf VLMs, multi-modal RAG, and self-refinement to generate ABC notation from image descriptions. Performance claims rest on separate human studies and machine evaluations rather than any derivation that reduces outputs to fitted parameters, self-defined quantities, or load-bearing self-citations. No equations, uniqueness theorems, or ansatzes are introduced that loop back to the method's own inputs by construction. The approach is therefore self-contained as an empirical application of existing components.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that pre-trained VLMs can be steered to output musically coherent ABC notation when augmented with retrieved examples; no free parameters or new invented entities are introduced in the abstract.

axioms (1)
  • domain assumption VLMs can generate valid ABC music notation from image-derived text prompts when guided by multi-modal retrieval and self-refinement.
    This premise underpins the zero-effort and high-quality claims and is invoked when the abstract states the VLM produces music using natural language.

pith-pipeline@v0.9.0 · 5792 in / 1357 out tokens · 47021 ms · 2026-05-18T12:36:41.513764+00:00 · methodology

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Reference graph

Works this paper leans on

35 extracted references · 35 canonical work pages · 1 internal anchor

  1. [1]

    Video background music generation with controllable music transformer,

    S. Di, Z. Jiang, S. Liu, Z. Wang, L. Zhu, Z. He, H. Liu, and S. Yan, “Video background music generation with controllable music transformer,” inProceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 2037–2045

    work page 2021

  2. [2]

    Mustango: Toward controllable text-to-music generation,

    J. Melechovsky, Z. Guo, D. Ghosal, N. Majumder, D. Herremans, and S. Poria, “Mustango: Toward controllable text-to-music generation,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024, pp. 8286–8309

    work page 2024

  3. [3]

    Music & consciousness: The evolution of guided imagery and music,

    H. L. Bonny and L. Summer, “Music & consciousness: The evolution of guided imagery and music,”(No Title), 2002

    work page 2002

  4. [4]

    Continuous emotion-based image-to- music generation,

    Y . Wang, M. Chen, and X. Li, “Continuous emotion-based image-to- music generation,”IEEE Transactions on Multimedia, vol. 26, pp. 5670– 5679, 2023

    work page 2023

  5. [5]

    Generating music from an image,

    G. C. Sergio, R. Mallipeddi, J.-S. Kang, and M. Lee, “Generating music from an image,” inProceedings of the 3rd International Conference on Human-Agent Interaction, 2015, pp. 213–216

    work page 2015

  6. [6]

    Automated music generation for visual art through emotion

    X. Tan, M. Antony, and H. Kong, “Automated music generation for visual art through emotion.” inICCC, 2020, pp. 247–250

    work page 2020

  7. [7]

    Emotion-guided image to music generation,

    S. Kundu, S. Singh, and Y . Iwahori, “Emotion-guided image to music generation,” inProceedings of the 2024 7th Artificial Intelligence and Cloud Computing Conference, 2024, pp. 323–330

    work page 2024

  8. [8]

    Bridging paintings and music– exploring emotion based music generation through paintings,

    T. Hisariya, H. Zhang, and J. Liang, “Bridging paintings and music– exploring emotion based music generation through paintings,”arXiv preprint arXiv:2409.07827, 2024

    work page arXiv 2024

  9. [9]

    Automatic stage lighting control: Is it a rule-driven process or generative task?

    Z. Zhao, D. Jin, Z. Zhou, and X. Zhang, “Automatic stage lighting control: Is it a rule-driven process or generative task?”arXiv preprint arXiv:2506.01482, 2025

    work page arXiv 2025

  10. [10]

    Illuminating music: Impact of color hue for background lighting on emotional arousal in piano performance videos,

    J. McDonald, S. Canazza, A. Chmiel, G. De Poli, E. Houbert, M. Murari, A. Rod `a, E. Schubert, and J. D. Zhang, “Illuminating music: Impact of color hue for background lighting on emotional arousal in piano performance videos,”Frontiers in Psychology, vol. 13, p. 828699, 2022

    work page 2022

  11. [11]

    M2ugen: Multi-modal music understanding and generation with the power of large language models,

    S. Liu, A. S. Hussain, C. Sun, and Y . Shan, “M2ugen: Multi-modal music understanding and generation with the power of large language models,”arXiv preprint arXiv:2311.11255, 2023

    work page arXiv 2023

  12. [12]

    Melfusion: Synthesizing music from image and language cues using diffusion models,

    S. Chowdhury, S. Nag, K. Joseph, B. V . Srinivasan, and D. Manocha, “Melfusion: Synthesizing music from image and language cues using diffusion models,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 26 826–26 835

    work page 2024

  13. [13]

    Multimodal music generation with explicit bridges and retrieval augmentation,

    B. Wang, L. Zhuo, Z. Wang, C. Bao, W. Chengjing, X. Nie, J. Dai, J. Han, Y . Liao, and S. Liu, “Multimodal music generation with explicit bridges and retrieval augmentation,”arXiv preprint arXiv:2412.09428, 2024

    work page arXiv 2024

  14. [14]

    Mumu-llama: Multi- modal music understanding and generation via large language models,

    S. Liu, A. S. Hussain, Q. Wu, C. Sun, and Y . Shan, “Mumu-llama: Multi- modal music understanding and generation via large language models,” arXiv preprint arXiv:2412.06660, vol. 3, no. 5, p. 6, 2024

    work page arXiv 2024

  15. [15]

    Xmusic: Towards a generalized and controllable symbolic music generation framework,

    S. Tian, C. Zhang, W. Yuan, W. Tan, and W. Zhu, “Xmusic: Towards a generalized and controllable symbolic music generation framework,” IEEE Transactions on Multimedia, no. 99, pp. 1–15, 2025

    work page 2025

  16. [16]

    A survey on deep learning for symbolic mu- sic generation: Representations, algorithms, evaluations, and challenges,

    S. Ji, X. Yang, and J. Luo, “A survey on deep learning for symbolic mu- sic generation: Representations, algorithms, evaluations, and challenges,” ACM Computing Surveys, vol. 56, no. 1, pp. 1–39, 2023

    work page 2023

  17. [17]

    An overview of domain-specific foundation model: key technologies, applications and challenges,

    H. Chen, H. Chen, Z. Zhao, K. Han, G. Zhu, Y . Zhao, Y . Du, W. Xu, and Q. Shi, “An overview of domain-specific foundation model: key technologies, applications and challenges,”Science China Information Sciences, 2025

    work page 2025

  18. [18]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clarket al., “Learning transferable visual models from natural language supervision,” inInternational conference on machine learning. PmLR, 2021, pp. 8748–8763

    work page 2021

  19. [19]

    Each to their own: Exploring the optimal embedding in rag,

    S. Chen, Z. Zhao, and J. Chen, “Each to their own: Exploring the optimal embedding in rag,”arXiv preprint arXiv:2507.17442, 2025

    work page arXiv 2025

  20. [20]

    Towards advanced mathematical reasoning for llms via first-order logic theorem proving,

    C. Cao, M. Li, J. Dai, J. Yang, Z. Zhao, S. Zhang, W. Shi, C. Liu, S. Han, and Y . Guo, “Towards advanced mathematical reasoning for llms via first-order logic theorem proving,”arXiv preprint arXiv:2506.17104, 2025

    work page arXiv 2025

  21. [21]

    Muspy: A toolkit for symbolic music generation,

    H.-W. Dong, K. Chen, J. McAuley, and T. Berg-Kirkpatrick, “Muspy: A toolkit for symbolic music generation,”arXiv preprint arXiv:2008.01951, 2020

    work page arXiv 2008

  22. [22]

    On the evaluation of generative models in music,

    L.-C. Yang and A. Lerch, “On the evaluation of generative models in music,”Neural Computing and Applications, vol. 32, no. 9, pp. 4773– 4784, 2020

    work page 2020

  23. [23]

    Mining limited data sufficiently: A bert-inspired approach for csi time series application in wireless communication and sensing,

    Z. Zhao, F. Meng, H. Li, X. Li, and G. Zhu, “Mining limited data sufficiently: A bert-inspired approach for csi time series application in wireless communication and sensing,”arXiv preprint arXiv:2412.06861, 2024

    work page arXiv 2024

  24. [24]

    Mozart’s touch: a lightweight multimodal music generation framework based on pre- trained large models,

    J. Li, T. Xu, X. Chen, X. Yao, J. Han, and S. Liu, “Mozart’s touch: a lightweight multimodal music generation framework based on pre- trained large models,” inInternational Conference on AI-Generated Content (AIGC 2024), vol. 13649. SPIE, 2025, pp. 198–207

    work page 2024

  25. [25]

    Songeval: A benchmark dataset for song aesthetics evaluation,

    J. Yao, G. Ma, H. Xue, H. Chen, C. Hao, Y . Jiang, H. Liu, R. Yuan, J. Xu, W. Xueet al., “Songeval: A benchmark dataset for song aesthetics evaluation,”arXiv preprint arXiv:2505.10793, 2025

    work page arXiv 2025

  26. [26]

    arXiv, abs/2507.01949

    K. K. Team, “Kwai keye-vl technical report,” 2025. [Online]. Available: https://arxiv.org/abs/2507.01949

    work page arXiv 2025

  27. [27]

    Long-clip: Unlocking the long-text capability of clip,

    B. Zhang, P. Zhang, X. Dong, Y . Zang, and J. Wang, “Long-clip: Unlock- ing the long-text capability of clip,”arXiv preprint arXiv:2403.15378, 2024

    work page arXiv 2024

  28. [28]

    Midicaps: A large-scale midi dataset with text captions,

    J. Melechovsky, A. Roy, and D. Herremans, “Midicaps: A large-scale midi dataset with text captions,”arXiv preprint arXiv:2406.02255, 2024

    work page arXiv 2024

  29. [29]

    PyTorch: An Imperative Style, High-Performance Deep Learning Library

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antigaet al., “Pytorch: An imperative style, high-performance deep learning library. arxiv 2019,” arXiv preprint arXiv:1912.01703, vol. 10, 1912

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  30. [30]

    Building a large scale dataset for image emotion recognition: The fine print and the benchmark,

    Q. You, J. Luo, H. Jin, and J. Yang, “Building a large scale dataset for image emotion recognition: The fine print and the benchmark,” in Proceedings of the AAAI conference on artificial intelligence, vol. 30, no. 1, 2016

    work page 2016

  31. [31]

    Vision-to-music generation: A survey,

    Z. Wang, C. Bao, L. Zhuo, J. Han, Y . Yue, Y . Tang, V . S.-J. Huang, and Y . Liao, “Vision-to-music generation: A survey,”arXiv preprint arXiv:2503.21254, 2025

    work page arXiv 2025

  32. [32]

    A survey on music generation from single-modal, cross-modal, and multi-modal perspectives,

    S. Li, S. Ji, Z. Wang, S. Wu, J. Yu, and K. Zhang, “A survey on music generation from single-modal, cross-modal, and multi-modal perspectives,”arXiv preprint arXiv:2504.00837, 2025

    work page arXiv 2025

  33. [33]

    Available: https://grok.com/

    xAI, “Grok.” [Online]. Available: https://grok.com/

    work page

  34. [34]

    Video background music generation: Dataset, method and evaluation,

    L. Zhuo, Z. Wang, B. Wang, Y . Liao, C. Bao, S. Peng, S. Han, A. Zhang, F. Fang, and S. Liu, “Video background music generation: Dataset, method and evaluation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 15 637–15 647

    work page 2023

  35. [35]

    Pianobart: Symbolic piano music generation and understanding with large-scale pre-training,

    X. Liang, Z. Zhao, W. Zeng, Y . He, F. He, Y . Wang, and C. Gao, “Pianobart: Symbolic piano music generation and understanding with large-scale pre-training,” in2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024, pp. 1–6

    work page 2024