Training-Free Multimodal Guidance for Video to Audio Generation

2025-09-29

AI Summary

Existing video-to-audio (V2A) generation methods either rely on large-scale paired datasets for joint training or model only pairwise cross-modal similarity, failing to ensure global multimodal consistency. To address this, we propose a training-free Multimodal Diffusion Guidance (MDG) mechanism that, for the first time, leverages the geometric volume relationship formed by video, audio, and text embeddings in a shared high-dimensional space to achieve unified cross-modal alignment. MDG is plug-and-play—compatible with any pre-trained audio diffusion model without fine-tuning. Extensive experiments on VGGSound and AudioCaps demonstrate that our method significantly improves the realism and semantic fidelity of generated audio. Quantitatively and perceptually, it outperforms both existing training-free and supervised approaches in audio quality and multimodal consistency, establishing a new state-of-the-art for zero-shot V2A generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Video-to-audio (V2A) generation aims to synthesize realistic and semantically aligned audio from silent videos, with potential applications in video editing, Foley sound design, and assistive multimedia. Although the excellent results, existing approaches either require costly joint training on large-scale paired datasets or rely on pairwise similarities that may fail to capture global multimodal coherence. In this work, we propose a novel training-free multimodal guidance mechanism for V2A diffusion that leverages the volume spanned by the modality embeddings to enforce unified alignment across video, audio, and text. The proposed multimodal diffusion guidance (MDG) provides a lightweight, plug-and-play control signal that can be applied on top of any pretrained audio diffusion model without retraining. Experiments on VGGSound and AudioCaps demonstrate that our MDG consistently improves perceptual quality and multimodal alignment compared to baselines, proving the effectiveness of a joint multimodal guidance for V2A.

Problem

• Training-free multimodal guidance for video-to-audio generation
• Enforcing unified alignment across video, audio, and text modalities
• Improving perceptual quality without retraining pretrained audio models

Innovation

• Training-free multimodal guidance for video-to-audio generation
• Uses modality embedding volume for unified alignment
• Plug-and-play control for pretrained audio diffusion models