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arxiv: 2503.14505 · v3 · submitted 2025-03-18 · 💻 cs.CV · cs.AI· cs.LG

MusicInfuser: Making Video Diffusion Listen and Dance

Susung Hong , Ira Kemelmacher-Shlizerman , Brian Curless , Steven M. Seitz This is my paper

Pith reviewed 2026-05-22 23:24 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords video diffusionmusic synchronizationdance video generationlayer selectioninfluence functionmodel adaptationtext-to-video
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The pith

Pre-trained text-to-video diffusion models can be adapted to generate dance videos synchronized with music by selectively fine-tuning layers identified via a constructive influence function.

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

MusicInfuser shows that existing video diffusion models can be aligned with musical inputs to produce dance videos that match the rhythm and style of given music tracks. The method avoids training a new model from scratch by using a criterion to pick which layers to update, based on how much they can be influenced without losing their original video knowledge. This allows efficient adaptation even with small specialized datasets and no motion data. The result is videos that generate new dance moves in response to music and work on new songs or longer clips. Readers care because it turns general-purpose video generators into music-responsive ones with minimal effort.

Core claim

MusicInfuser adapts pre-trained text-to-video diffusion models to generate high-quality dance videos synchronized with specified music tracks. It uses a novel layer-wise adaptability criterion based on a guidance-inspired constructive influence function to select adaptable layers, reducing training costs while preserving rich prior knowledge even with limited datasets. This is done without requiring motion data.

What carries the argument

Layer-wise adaptability criterion based on a guidance-inspired constructive influence function that selects which layers to adapt.

If this is right

  • Generates novel and diverse dance movements that respond dynamically to music.
  • Generalizes well to unseen music tracks, longer video sequences, and unconventional subjects.
  • Outperforms baseline models in consistency and synchronization.
  • Trains on a single GPU within a day without motion data.

Where Pith is reading between the lines

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

  • This selective layer adaptation could be applied to align video models with other inputs like speech or sound effects.
  • Similar influence-based selection might help adapt diffusion models in other domains with limited data.
  • The method suggests that pre-trained models have modular knowledge that can be targeted for specific tasks like music response.

Load-bearing premise

The guidance-inspired constructive influence function can accurately identify layers adaptable to music inputs while keeping the model's video generation abilities intact.

What would settle it

If adapted models produce dance videos that do not match the timing or style of the input music on new tracks, or if all layers must be retrained to achieve synchronization, the approach would not hold.

Figures

Figures reproduced from arXiv: 2503.14505 by Brian Curless, Ira Kemelmacher-Shlizerman, Steven M. Seitz, Susung Hong.

Figure 1
Figure 1. Figure 1: MusicInfuser adapts video diffusion models to music, making them listen and dance according to the music. This adaptation is done in a prior-preserving manner, enabling it to also accept style through the prompt while aligning the movement to the music. Abstract We introduce MusicInfuser, an approach that aligns pre￾trained text-to-video diffusion models to generate high￾quality dance videos synchronized w… view at source ↗
Figure 2
Figure 2. Figure 2: Motivational example. Skeletal motion generation [ [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Using prompts such as “a {marmot, rabbit, dog (top to bottom rows)} dancing ...,” our method generalizes to unseen dancing subjects. to specify dance style, setting, and other aesthetics ( [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: We can generate group dance videos aligned with music, [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Zero-initialized cross-attention (ZICA) block. The output projection is initialized with a zero matrix, making the cross-attention [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Generalization capabilities in terms of music length and type. MusicInfuser can generate multiple times longer dance videos [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Speed control. The audio input is slowed down (top row) or sped up (bottom row) by factors of 0.75 and 1.25, respectively. This [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Human evaluation. quality, music-dance alignment, motion realism, and chore￾ography complexity. The human evaluation demonstrates that our approach consistently outperforms previous work, with evaluators particularly noting improvements in video quality and movement naturalness. The details are provided in the supplementary material. Ablation Studies In [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 8
Figure 8. Figure 8: By changing the seed, our method can produce diverse [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of audio-driven generation with MM-Diffusion [ [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: MusicInfuser infuses listening capability into the text-to-video model (Mochi [ [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Changes in the complexity of choreography. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Ablation study. The prompt is set to “a male dancer dancing in an art gallery with some paintings, captured from a front view”. [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Ablation study. The prompt is set to “a male dancer wearing a suit dancing in the middle of a New York City, captured from a [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Layer adaptability graph from [26], showing imaging and aesthetic quality. H. Beta-Uniform Scheduling The visualization of the Beta-Uniform scheduling strategy is shown in [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Beta distributions. Test Music Code Genre mLH4 LA style Hip-hop mKR2 Krump mBR0 Break mLO2 Lock mJB5 Ballet Jazz mWA0 Waack mJS3 Street Jazz mMH3 Middle Hip-hop mHO5 House mPO1 Pop [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Failure cases. Our model inherits some issues from the base model, such as failing to generate fine details (e.g., fingers and [PITH_FULL_IMAGE:figures/full_fig_p016_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: More music-and-text-to-video generation results. [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
read the original abstract

We introduce MusicInfuser, an approach that aligns pre-trained text-to-video diffusion models to generate high-quality dance videos synchronized with specified music tracks. Rather than training a multimodal audio-video or audio-motion model from scratch, our method demonstrates how existing video diffusion models can be efficiently adapted to align with musical inputs. We propose a novel layer-wise adaptability criterion based on a guidance-inspired constructive influence function to select adaptable layers, significantly reducing training costs while preserving rich prior knowledge, even with limited, specialized datasets. Experiments show that MusicInfuser effectively bridges the gap between music and video, generating novel and diverse dance movements that respond dynamically to music. Furthermore, our framework generalizes well to unseen music tracks, longer video sequences, and unconventional subjects, outperforming baseline models in consistency and synchronization. All of this is achieved without requiring motion data, with training completed on a single GPU within a day.

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

2 major / 1 minor

Summary. The manuscript introduces MusicInfuser, a technique for adapting pre-trained text-to-video diffusion models to generate dance videos synchronized with input music tracks. It proposes a novel layer-wise adaptability criterion based on a guidance-inspired constructive influence function to select which layers to adapt, with the goal of reducing training costs while preserving prior knowledge even on limited specialized datasets. The paper claims this enables generation of novel and diverse dance movements that respond dynamically to music, strong generalization to unseen music tracks, longer sequences, and unconventional subjects, and outperformance of baselines in consistency and synchronization—all without motion data and with single-GPU training completed in one day.

Significance. If the central claims are substantiated with quantitative evidence and the adaptability criterion is shown to be principled rather than heuristic, the work would be significant for efficient multimodal adaptation of large diffusion models. Demonstrating that targeted layer selection can achieve music-video alignment and generalization on limited data without full retraining or motion supervision would be relevant to resource-efficient generative modeling in computer vision.

major comments (2)
  1. [Abstract] Abstract: The central experimental claims—that the method outperforms baselines in consistency and synchronization, generalizes to unseen music tracks/longer sequences/unconventional subjects, and produces novel diverse movements—are stated without any metrics, baseline names, dataset descriptions, quantitative results, or error analysis. These assertions are load-bearing for the paper's contribution but cannot be evaluated from the given information.
  2. [Method (layer-wise adaptability criterion)] Method (layer-wise adaptability criterion): The novel layer-wise adaptability criterion relies on an unspecified 'guidance-inspired constructive influence function' whose ability to select adaptable layers while preserving priors on limited data is asserted without definition, mathematical formulation, derivation, computation details, or ablation against simpler selection strategies. This is load-bearing for the efficiency, low-cost training, and generalization claims.
minor comments (1)
  1. [Abstract] Abstract: The term 'guidance-inspired constructive influence function' is introduced without any reference or prior explanation of the underlying guidance mechanism.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment below and will revise the manuscript accordingly to improve clarity and substantiation of the claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central experimental claims—that the method outperforms baselines in consistency and synchronization, generalizes to unseen music tracks/longer sequences/unconventional subjects, and produces novel diverse movements—are stated without any metrics, baseline names, dataset descriptions, quantitative results, or error analysis. These assertions are load-bearing for the paper's contribution but cannot be evaluated from the given information.

    Authors: We agree that the abstract presents claims at a high level without quantitative details. While full experimental results, metrics, baselines, datasets, and analysis appear in Sections 4 and 5, we will revise the abstract to incorporate key quantitative highlights (e.g., specific synchronization metrics and baseline names) to make the claims more evaluable from the abstract alone. revision: yes

  2. Referee: [Method (layer-wise adaptability criterion)] Method (layer-wise adaptability criterion): The novel layer-wise adaptability criterion relies on an unspecified 'guidance-inspired constructive influence function' whose ability to select adaptable layers while preserving priors on limited data is asserted without definition, mathematical formulation, derivation, computation details, or ablation against simpler selection strategies. This is load-bearing for the efficiency, low-cost training, and generalization claims.

    Authors: The guidance-inspired constructive influence function and layer-wise criterion are defined with mathematical formulation in Section 3.2. We will expand this section with additional derivation steps, explicit computation details, and an ablation study against simpler strategies (e.g., full-layer adaptation or random selection) to better demonstrate its role in efficiency and prior preservation. revision: yes

Circularity Check

0 steps flagged

No circularity; adaptation method is independent of reported outcomes

full rationale

The paper introduces MusicInfuser as an adaptation of pre-trained text-to-video diffusion models via a novel layer-wise adaptability criterion using a guidance-inspired constructive influence function. This criterion is presented as an input to the method rather than derived from the target synchronization or generalization results. No equations or steps in the provided abstract or description reduce predictions to fitted parameters by construction, nor do self-citations form a load-bearing chain for the central claims. The framework is framed as a general adaptation technique whose effectiveness is evaluated experimentally on unseen data, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract; no explicit free parameters, axioms, or invented entities are described.

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