AV-Edit: Multimodal Generative Sound Effect Editing via Audio-Visual Semantic Joint Control

• URL: http://arxiv.org/abs/2511.21146v1
• Date: Wed, 26 Nov 2025 07:59:53 GMT
• Title: AV-Edit: Multimodal Generative Sound Effect Editing via Audio-Visual Semantic Joint Control
• Authors: Xinyue Guo, Xiaoran Yang, Lipan Zhang, Jianxuan Yang, Zhao Wang, Jian Luan,
• Abstract summary: AV-Edit is a generative sound effect editing framework that enables fine-grained editing of existing audio tracks in videos.<n>The proposed method employs a specially designed contrastive audio-visual masking autoencoder (CAV-MAE-Edit) for multimodal pre-training.<n> Experiments demonstrate that the proposed AV-Edit generates high-quality audio with precise modifications based on visual content.
• Score: 10.55114688654566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sound effect editing-modifying audio by adding, removing, or replacing elements-remains constrained by existing approaches that rely solely on low-level signal processing or coarse text prompts, often resulting in limited flexibility and suboptimal audio quality. To address this, we propose AV-Edit, a generative sound effect editing framework that enables fine-grained editing of existing audio tracks in videos by jointly leveraging visual, audio, and text semantics. Specifically, the proposed method employs a specially designed contrastive audio-visual masking autoencoder (CAV-MAE-Edit) for multimodal pre-training, learning aligned cross-modal representations. These representations are then used to train an editorial Multimodal Diffusion Transformer (MM-DiT) capable of removing visually irrelevant sounds and generating missing audio elements consistent with video content through a correlation-based feature gating training strategy. Furthermore, we construct a dedicated video-based sound editing dataset as an evaluation benchmark. Experiments demonstrate that the proposed AV-Edit generates high-quality audio with precise modifications based on visual content, achieving state-of-the-art performance in the field of sound effect editing and exhibiting strong competitiveness in the domain of audio generation.

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