Video-Guided Foley Sound Generation with Multimodal Controls (2411.17698v4)

Abstract: Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model designed for video-guided sound generation that supports multimodal conditioning through text, audio, and video. Given a silent video and a text prompt, MultiFoley allows users to create clean sounds (e.g., skateboard wheels spinning without wind noise) or more whimsical sounds (e.g., making a lion's roar sound like a cat's meow). MultiFoley also allows users to choose reference audio from sound effects (SFX) libraries or partial videos for conditioning. A key novelty of our model lies in its joint training on both internet video datasets with low-quality audio and professional SFX recordings, enabling high-quality, full-bandwidth (48kHz) audio generation. Through automated evaluations and human studies, we demonstrate that MultiFoley successfully generates synchronized high-quality sounds across varied conditional inputs and outperforms existing methods. Please see our project page for video results: https://ificl.github.io/MultiFoley/

• The paper introduces a novel multimodal system that synthesizes Foley sounds using video guidance and controls via text, audio, and video.
• It leverages a diffusion-based generative architecture combining a Diffusion Transformer with a high-quality audio autoencoder to ensure precise synchronization.
• Validated by quantitative benchmarks and human studies, MultiFoley offers enhanced sound quality and robust performance across varied audio environments.

Video-Guided Foley Sound Generation with Multimodal Controls: An Analytical Overview

The paper "Video-Guided Foley Sound Generation with Multimodal Controls" by Ziyang Chen et al. presents MultiFoley, a sophisticated system for generating Foley sounds guided by video input, while offering multimodal controls via text, audio, and video. This research addresses the creative demands of sound design, particularly Foley sound effects, by introducing a model that allows precise, user-directed audio generation.

At its core, MultiFoley is built upon a diffusion-based generative architecture, employing a hierarchical framework that integrates a Diffusion Transformer (DiT) and a high-quality audio autoencoder to produce high-fidelity, synchronized sounds. The novelty lies in the model's ability to accept a variety of conditioning inputs—text, audio, and video—which significantly broadens the scope for creative expression and precision in sound design.

Key Contributions

1. Multimodal Framework: The system synthesizes audio for silent video content through multimodal inputs, including textual descriptions, sonic attributes from reference audio, and visual cues from video. This integration allows a versatile sound design process where users can specify precise sound characteristics or adopt creative auditory profiles.
2. Joint Dataset Training: A notable methodological innovation is the model's training regimen, which spans both low-quality internet video datasets and high-fidelity professional sound libraries. This approach ensures the model is robust across a spectrum of audio environments, generating outputs that maintain professional sound standards.
3. Diverse Foley Applications: MultiFoley supports an array of applications, from cleanly synchronized onomatopoeic Foley generation to more adventurous applications such as replacing a lion's roar with a cat's meow, supported by text control. Such applications are facilitated through a fine-tuned balance of cross-modal associations between audio, text, and visual stimuli.
4. Performance and Human Study Validation: Through rigorous quantitative benchmarks and qualitative human studies, the model's efficacy is evident. MultiFoley exhibits improved performance in synchronization and sound quality metrics compared to existing methods, highlighting its capacity for high-definition, aligned sound generation.

Implications

The implications of this work are manifold, both in practical and theoretical domains. Practically, MultiFoley provides sound designers and multimedia artists with a tool that not only automates but significantly enhances the creative possibilities for sound generation aligned with visual content. This can dramatically reduce the production costs and time associated with traditional Foley sound design, which is often manual and labor-intensive.

Theoretically, the integration of text, audio, and video conditioning exemplifies an advanced approach to multimodal machine learning, where the boundaries of modalities are traversed to create coherent and contextually rich outputs. This can stimulate further research into cross-modal generative models, broadening the understanding and capabilities of machine learning systems in unified perception and generation tasks.

Future Prospects

Despite its strengths, MultiFoley highlights certain challenges, such as handling overlapping sound events and refining the quality of disparate audio sources. Future research could explore larger, more comprehensive datasets to augment the model's versatility and performance. Improving the model's ability to intricately balance multiple sound sources in a cohesive manner remains an area ripe for exploration.

Overall, this paper presents a robust and versatile approach to Foley sound generation, demonstrating significant advancements in how machines interpret and generate auditory content in a multimodal context. The advancements in MultiFoley underscore a shift toward greater flexibility and precision in digital audio creation, potentially redefining sound design methodologies in multimedia production.