Sound Sparks Motion: Audio and Text Tuning for Video Editing
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 ...
| Authors | AmirHossein Naghi Razlighi, Aryan Mikaeili, Ali Mahdavi-Amiri et al. |
|---|---|
| Primary category | cs.GR |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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