pith. sign in

arxiv: 2605.15307 · v1 · pith:IB4G5DUOnew · submitted 2026-05-14 · 💻 cs.GR · cs.CV· cs.MM· cs.SD

Sound Sparks Motion: Audio and Text Tuning for Video Editing

AmirHossein Naghi Razlighi , Aryan Mikaeili , Ali Mahdavi-Amiri , Daniel Cohen-Or , Yiorgos Chrysanthou This is my paper

Pith reviewed 2026-05-19 15:41 UTC · model grok-4.3

classification 💻 cs.GR cs.CVcs.MMcs.SD
keywords video editingmotion editingtest-time tuningmultimodal conditioningaudio-visual generationvision-language modelsgenerative models
0
0 comments X

The pith

Tuning an audio latent and text residual at test time lets video generation models realize specific motions that text prompts alone cannot produce.

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

This paper presents a training-free way to edit motions in videos by tweaking the model's audio and text conditioning signals during use rather than retraining it. The process adjusts an audio representation taken from the original clip along with a small adjustment to the text description, using a vision-language model to check if the wanted action shows up in the result. This helps overcome the models' common weakness in handling detailed actions when relying only on text instructions. Readers might find this useful for creating more controlled video content with less effort. The approach also demonstrates that these adjustments can transfer to edit motions in different videos.

Core claim

The central discovery is that tuning only two lightweight variables—an audio latent derived from the source video and a residual perturbation in the text-conditioning—while using vision-language model feedback to verify motion presence, enables effective motion editing in large generative video models that typically struggle with specific localized actions under prompt-only control.

What carries the argument

Test-time tuning of multimodal conditioning signals consisting of an audio latent and text residual, supervised by vision-language model semantic feedback.

Load-bearing premise

The vision-language model reliably detects the presence of the intended motion in generated videos to provide useful guidance for the tuning process.

What would settle it

A comparison experiment where videos tuned with this method show equivalent or worse motion realization compared to prompt-only control, as judged by independent human evaluators or an automated motion detection system.

Figures

Figures reproduced from arXiv: 2605.15307 by Ali Mahdavi-Amiri, AmirHossein Naghi Razlighi, Aryan Mikaeili, Daniel Cohen-Or, Yiorgos Chrysanthou.

Figure 1
Figure 1. Figure 1: Given a source video and an edit prompt, our method tunes the audio latent as a conditioning parameter to the LTX video model, such that the desired [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A visualization of average attention heat maps for tuned audio and [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of Sound Sparks Motion. Given source audio, reference video frames, and an edit prompt, our method keeps LTX Retake, Text Encoder, Qwen, and the VAE modules frozen, and optimizes only the audio latent and a residual text-conditioning vector. The edited video is temporally sampled and scored by Qwen2.5-VL through a binary motion question, producing a test-time loss that updates only the learnable l… view at source ↗
Figure 4
Figure 4. Figure 4: A comparison between text-only residual tuning and tuning text and [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of supervision method. CLIP [Radford et al. 2021] guidance primarily optimizes frame-level semantic appearance rather than temporal action, leading to a mostly static yawning state. XCLIP [Ma et al. 2022] provides limited temporal awareness, but still introduces global appearance drift and localized artifacts, highlighted by the red box. Qwen-based su￾pervision better captures whether the requested … view at source ↗
Figure 7
Figure 7. Figure 7: Transferability of optimized latents. Learned audio and residual text latents are reused on related target prompts without additional tuning, transferring motions such as yawning, shouting, and wing opening across different subjects and scenes. Note that in the last row although the latents are initially learned for a realistic video, they can be transferred to a cartoonish video, which shows the generalit… view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative Comparisons of Sound Sparks Motion vs various different editing methods on diverse set of edits [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative Comparisons of Sound Sparks Motion vs LTX2.3 Retake on diverse set of edits. Red boxes show abrupt changes or magical insertions or obvious artifacts in the LTX Retake method [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
read the original abstract

Motion-centric video editing remains difficult for large generative video models, which often respond well to appearance changes but struggle to produce specific, localized actions or state transitions in an existing clip. We introduce Sound Sparks Motion, a training-free framework that enables motion editing in an audio-visual video generation model by tuning its internal multimodal conditioning signals at test time. Rather than modifying model weights, our method tunes only two lightweight variables: an audio latent derived from the source video and a residual perturbation in the text-conditioning. We find that this combination can encourage motion edits that the underlying model often struggles to realize under prompt-only control. Since there is no direct way to evaluate temporal alignment between text and motion, we guide the tuning process using a vision-language model that provides feedback indicating whether the intended motion appears in the generated video. This simple supervision yields an effective semantic objective for motion editing, while regularization and perceptual-temporal constraints help preserve content and visual quality. Beyond per-video tuning, we show that the learned latent controls are transferable across videos, suggesting that they capture reusable motion-edit directions rather than overfitting to a single example. Our results highlight multimodal conditioning tuning, particularly through the audio pathway, as a promising direction for motion-aware video editing, and suggest that test-time tuning can serve as a lightweight probing mechanism that helps reveal latent motion controls embedded in the model's multimodal conditioning. Code and data are available via our project page: https://amirhossein-razlighi.github.io/Sound_Sparks_Motion/

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 / 2 minor

Summary. The paper introduces Sound Sparks Motion, a training-free framework for motion-centric video editing in audio-visual generative models. It tunes two lightweight variables—an audio latent derived from the source video and a residual perturbation to the text conditioning—at test time. The tuning is driven by a vision-language model (VLM) that supplies a semantic objective indicating whether the target motion appears in the generated video, combined with regularization and perceptual-temporal constraints to preserve content and quality. The authors claim this enables localized motion edits that prompt-only control struggles to realize, demonstrate transferability of the learned latents across videos, and position the approach as a lightweight probe for latent motion controls in multimodal conditioning.

Significance. If the VLM-guided tuning reliably produces the intended motion dynamics while maintaining visual fidelity, the work would establish a practical test-time optimization route for motion editing that leverages audio pathways without retraining. The training-free nature, open code, and suggestion of reusable motion-edit directions would make it a useful contribution to generative video editing, particularly for cases where standard prompting fails to control temporal actions.

major comments (2)
  1. [§3.2, Eq. (4)] §3.2, Eq. (4): the loss L = L_VLM + λ_reg * R defines the sole semantic objective via VLM binary/scalar feedback on motion presence, yet the manuscript reports no correlation study between these VLM scores and human motion ratings, frame-wise action detection, or any direct temporal metric such as optical-flow consistency. This leaves open the possibility that optimization converges to VLM appearance cues rather than the localized dynamics the method claims to control.
  2. [Section 4] Section 4 (results): the central claim that the tuned combination outperforms prompt-only control and yields transferable motion directions requires quantitative support via ablation studies, baseline comparisons, and metrics that isolate motion accuracy; without these, the empirical grounding for the motion-editing effectiveness remains insufficient to substantiate the abstract's assertions.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by briefly stating the key quantitative findings or metrics that support the motion-editing and transferability claims.
  2. [Section 3] Notation for the audio latent and text residual perturbation should be introduced with explicit symbols in Section 3 to improve readability of the optimization procedure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and commit to revisions that strengthen the validation and empirical support for our claims.

read point-by-point responses

  1. Referee: [§3.2, Eq. (4)] §3.2, Eq. (4): the loss L = L_VLM + λ_reg * R defines the sole semantic objective via VLM binary/scalar feedback on motion presence, yet the manuscript reports no correlation study between these VLM scores and human motion ratings, frame-wise action detection, or any direct temporal metric such as optical-flow consistency. This leaves open the possibility that optimization converges to VLM appearance cues rather than the localized dynamics the method claims to control.

    Authors: We thank the referee for this important observation. The VLM objective is specifically prompted to evaluate the presence of the target motion (rather than static appearance), and the regularization and perceptual-temporal constraints are intended to keep content fixed while allowing motion changes. Nevertheless, we acknowledge that an explicit correlation analysis would provide stronger reassurance that optimization is driven by dynamics. In the revised manuscript we will add a dedicated analysis that correlates VLM scores with human motion ratings on a held-out set of examples and with simple temporal metrics such as optical-flow consistency, thereby addressing the concern that the method may be exploiting appearance cues. revision: yes

  2. Referee: [Section 4] Section 4 (results): the central claim that the tuned combination outperforms prompt-only control and yields transferable motion directions requires quantitative support via ablation studies, baseline comparisons, and metrics that isolate motion accuracy; without these, the empirical grounding for the motion-editing effectiveness remains insufficient to substantiate the abstract's assertions.

    Authors: We agree that quantitative evidence is necessary to substantiate the central claims. The current manuscript emphasizes qualitative results and transferability demonstrations. In the revision we will augment Section 4 with (i) ablation studies isolating the contribution of audio-latent tuning versus text-residual tuning, (ii) direct comparisons against prompt-only baselines, and (iii) quantitative metrics that focus on motion accuracy (e.g., action-recognition accuracy on the edited clips and motion-vector similarity). These additions will supply the rigorous empirical grounding requested. revision: yes

Circularity Check

0 steps flagged

No significant circularity: external VLM supervision and regularization keep the method self-contained

full rationale

The paper's core procedure tunes an audio latent and text residual to maximize a VLM-derived semantic objective for motion presence (L = L_VLM + λ_reg * R). This objective is supplied by an external vision-language model rather than being defined in terms of the tuned variables themselves. No equations reduce the target motion edit to a quantity fitted from the same parameters by construction, and no self-citation chain or imported uniqueness theorem is invoked to justify the choice of audio pathway or loss terms. The reported transferability across videos is an empirical observation, not a derived necessity. The derivation chain therefore remains independent of its own outputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim depends on the base generative model possessing tunable multimodal conditioning signals and on the VLM being able to detect motion presence accurately enough to drive optimization.

free parameters (2)
  • audio latent
    Derived from source video and optimized at test time to influence motion.
  • text-conditioning residual perturbation
    Small additive change tuned jointly with the audio latent.
axioms (2)
  • domain assumption The underlying video generation model contains internal multimodal conditioning signals that respond to small test-time adjustments.
    Invoked when stating that tuning audio latent and text residual can produce motion edits.
  • domain assumption A vision-language model can serve as a reliable proxy for detecting the presence of intended motion in generated frames.
    Used to define the semantic objective for the tuning process.

pith-pipeline@v0.9.0 · 5827 in / 1354 out tokens · 66739 ms · 2026-05-19T15:41:33.580174+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 4 internal anchors

  1. [1]

    Lora-edit: Controllable first-frame-guided video editing via mask-aware lora fine-tuning,

    Lora-edit: Controllable first-frame-guided video editing via mask-aware lora fine-tuning.arXiv preprint arXiv:2506.10082(2025). Chenjian Gao, Lihe Ding, Xin Cai, Zhanpeng Huang, Zibin Wang, and Tianfan Xue

    work page arXiv 2025

  2. [2]

    InThe Fourteenth International Conference on Learning Representations

    Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine- Tuning. InThe Fourteenth International Conference on Learning Representations. https://openreview.net/forum?id=xkRMJ1Y7Um Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, et al

    work page

  3. [3]

    Gemma 3 Technical Report. arXiv:2503.19786 [cs.CL] https://arxiv.org/abs/ 2503.19786 Daniel Geng, Charles Herrmann, Junhwa Hur, Forrester Cole, Serena Zhang, Tobias Pfaff, Tatiana Lopez-Guevara, Carl Doersch, Yusuf Aytar, Michael Rubinstein, Chen Sun, Oliver Wang, Andrew Owens, and Deqing Sun. 2024. Motion Prompting: Con- trolling Video Generation with Mo...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  4. [4]

    ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control

    ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control. arXiv preprint arXiv:2604.20816(2026). Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, Andrew Kvochko, Avishai Berkowitz, Daniel Shalem, Daphna Lifschitz, Dudu Moshe, Eitan Porat, Eitan Richardson, Guy Shiran, Itay Chachy, Jonathan Chetboun, Michael Finkelson, Michae...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2503.10592 2026

  5. [5]

    ReRoPE: Repurposing RoPE for Relative Camera Control.arXiv preprint arXiv:2602.08068(2026). doi:10.48550/arXiv.2602.08068 Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, et al

    work page doi:10.48550/arxiv.2602.08068 2026

  6. [6]

    InProceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Neural 3d video synthesis from multi-view video. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5521–5531. Yiqi Lin, Guoqiang Liang, Ziyun Zeng, Zechen Bai, Yanzhe Chen, and Mike Zheng Shou

    work page

  7. [7]

    Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance. arXiv:2603.02175 [cs.CV] https://arxiv.org/abs/2603.02175 Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Wenyu Qin, Menghan Xia, et al. 2025. Improving video generation with human feedback.arXiv preprint arXiv:2501.13918(2025). Sha...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  8. [8]

    doi:10.48550/arXiv.2405.13865 Koichi Namekata, Sherwin Bahmani, Ziyi Wu, Yash Kant, Igor Gilitschenski, and David B

    ReVideo: Re make a Video with Motion and Content Control.arXiv preprint arXiv:2405.13865(2024). doi:10.48550/arXiv.2405.13865 Koichi Namekata, Sherwin Bahmani, Ziyi Wu, Yash Kant, Igor Gilitschenski, and David B. Lindell. 2025. SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation. InThe Thirteenth International Conference on Learning Repres...

    work page doi:10.48550/arxiv.2405.13865 2024

  9. [9]

    arXiv:2310.03739 [cs.CV] Mihir Prabhudesai, Russell Mendonca, Zheyang Qin, Katerina Fragkiadaki, and Deepak Pathak

    Aligning Text-to-Image Diffusion Models with Reward Backpropagation. arXiv:2310.03739 [cs.CV] Mihir Prabhudesai, Russell Mendonca, Zheyang Qin, Katerina Fragkiadaki, and Deepak Pathak. 2024. Video Diffusion Alignment via Reward Gradients. arXiv:2407.08737 [cs.CV] https://arxiv.org/abs/2407.08737 Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao ...

    work page doi:10.48550/arxiv.2305.12328 2024

  10. [10]

    Open-Sora: Democratizing Efficient Video Production for All

    Stylemaster: Stylize your video with artistic generation and translation. In Proceedings of the Computer Vision and Pattern Recognition Conference. 2630–2640. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on co...

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  11. [11]

    One source video named`input_video`

    work page

  12. [12]

    edit_prompt

    Multiple edited candidate videos. Each candidate has a unique video name. Your task is to evaluate each candidate edited video against the source video and the edit prompt, then compare the candidates against each other.↩→ Edit prompt: `[Edit prompt]` Candidate video names: `[Video file names]` UniEdit's output is a gif file, on the left is input video, a...

    work page