Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis (2409.06135v1)

Abstract: Foley is a term commonly used in filmmaking, referring to the addition of daily sound effects to silent films or videos to enhance the auditory experience. Video-to-Audio (V2A), as a particular type of automatic foley task, presents inherent challenges related to audio-visual synchronization. These challenges encompass maintaining the content consistency between the input video and the generated audio, as well as the alignment of temporal and loudness properties within the video. To address these issues, we construct a controllable video-to-audio synthesis model, termed Draw an Audio, which supports multiple input instructions through drawn masks and loudness signals. To ensure content consistency between the synthesized audio and target video, we introduce the Mask-Attention Module (MAM), which employs masked video instruction to enable the model to focus on regions of interest. Additionally, we implement the Time-Loudness Module (TLM), which uses an auxiliary loudness signal to ensure the synthesis of sound that aligns with the video in both loudness and temporal dimensions. Furthermore, we have extended a large-scale V2A dataset, named VGGSound-Caption, by annotating caption prompts. Extensive experiments on challenging benchmarks across two large-scale V2A datasets verify Draw an Audio achieves the state-of-the-art. Project page: https://yannqi.github.io/Draw-an-Audio/.

• The paper introduces a multi-instruction model that integrates text, video, drawn masks, and loudness signals for enhanced audio-visual alignment.
• It employs a Mask-Attention Module and Time-Loudness Module to ensure semantic content consistency and synchronized audio dynamics.
• Experimental results demonstrate state-of-the-art performance on metrics like FD, IS, and CS-AV, advancing video-to-audio synthesis research.

Overview of "Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis"

The paper "Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis" presents an innovative approach to automatic video-to-audio (V2A) synthesis, focusing on enhancing audio-visual consistency across content, temporal, and loudness dimensions. The proposed framework, termed "Draw an Audio," introduces significant advancements in the domain of audio generation aligned tightly with the accompanying video content. This paper provides substantial contributions to audio synthesis by implementing a multiple-instruction synthesis model which supports diverse inputs, including text prompts, videos, drawn video masks, and loudness signals, overcoming several challenges faced by previous models.

Key Contributions and Methodological Advances

The authors address three major challenges in the V2A task: semantic content consistency, temporal synchronization, and loudness consistency. These aspects are crucial because misalignment between video and audio can severely disrupt the user experience.

1. Mask-Attention Module (MAM): This component enhances content consistency by employing drawn video masks, which allow the model to focus on regions of interest within the video. This ensures that generated sounds are more accurately tied to relevant visual elements, improving semantic alignment between audio and video.
2. Time-Loudness Module (TLM): To maintain temporal synchronization and ensure the loudness of generated audio is consistent with human auditory perception, TLM uses auxiliary loudness signals. These signals enable fine control over the audio intensity, allowing sounds to reflect actions in the video, such as footsteps growing louder as an object approaches the camera.
3. Integration of Multi-Instructions: The framework supports various inputs to generate a more cohesive audio experience. By leveraging text, video, and handcrafted loudness signals, the model can produce audio that adheres to diverse user-defined instructions, enhancing flexibility and control.

The authors utilize a latent diffusion model (LDM) to efficiently synthesize audio within these constraints, leveraging a cross-attention mechanism that aligns video segments with audio outputs. This approach aligns with the advancements in generative models seen in text-to-image and text-to-audio paradigms, but places a novel emphasis on the intersection of audio and video.

Experimental Validation and Results

Extensive experiments conducted on large-scale V2A datasets demonstrate the efficacy of the Draw an Audio framework. The results indicate that it achieves state-of-the-art performance across various metrics, significantly enhancing the synthesis quality compared to existing methods like DIFF-FOLEY and other baseline models. The paper reports improvements in measures such as Frechet Distance (FD), Inception Score (IS), and CLIP-Score of Audio and Video (CS-AV), showcasing the model's ability to produce semantically, temporally, and acoustically coherent audio.

Implications and Future Directions

This research contributes both theoretically and practically to the V2A generation task. The ability to synthesize audio that accurately aligns with video content opens up new possibilities in areas such as automatic video editing, film postproduction, and interactive media. The framework could be extended to more complex scenes or adapted for real-time generation scenarios.

Future work may explore the integration with more sophisticated machine learning frameworks, enhancing the model's ability to generalize across varied datasets and explore applications beyond traditional media, like virtual and augmented reality. Moreover, developing mechanisms to automatically generate video masks and loudness signals can further streamline the synthesis process, reducing the need for manual inputs and facilitating broader adoption.

In conclusion, the paper makes a significant advancement in the field of video-to-audio synthesis by addressing fundamental challenges and introducing a robust, multi-instructional framework. It paves the way for further exploration and innovation in creating immersive audio-visual experiences.