Music2Video: Automatic Generation of Music Video with fusion of audio and text (2201.03809v2)

Abstract: Creation of images using generative adversarial networks has been widely adapted into multi-modal regime with the advent of multi-modal representation models pre-trained on large corpus. Various modalities sharing a common representation space could be utilized to guide the generative models to create images from text or even from audio source. Departing from the previous methods that solely rely on either text or audio, we exploit the expressiveness of both modality. Based on the fusion of text and audio, we create video whose content is consistent with the distinct modalities that are provided. A simple approach to automatically segment the video into variable length intervals and maintain time consistency in generated video is part of our method. Our proposed framework for generating music video shows promising results in application level where users can interactively feed in music source and text source to create artistic music videos. Our code is available at https://github.com/joeljang/music2video.

• The paper introduces an innovative fusion method that integrates audio and text inputs via a shared representation space to guide music video generation.
• It employs dynamic video segmentation based on musical onset and beat detection to adjust visual transitions in response to audio intensity changes.
• The methodology ensures time consistency through iterative optimization and latent vector regularization, enhancing narrative coherence across frames.

Overview of "Music2Video: Automatic Generation of Music Video with Fusion of Audio and Text"

The paper "Music2Video: Automatic Generation of Music Video with Fusion of Audio and Text" introduces a novel framework for the automatic creation of music videos by leveraging a fusion of audio and text inputs. This work builds on the advances in generative adversarial networks (GANs) and their application to multi-modal generation tasks, where various inputs such as images, text, and audio share a common representation space. By integrating these modalities, the authors propose a method that produces video content consistent with and inspired by the given audio and text material.

Technical Contributions

The authors outline two primary contributions in their paper:

1. Integration of Audio and Text Guidance: The paper describes an innovative method for integrating audio and textual inputs to guide the generation of music videos. This approach aims to resolve the challenge of conflicting visualizations that occur when naively combining these inputs.
2. Dynamic Video Segmentation: The framework includes an automatic video segmentation process based on musical dynamics. The segmentation facilitates the adaptive transition of video scenes to match the themes and intensity shifts within the music, addressing the challenge of fixed-interval segmentation.

Methodology

The methodology is rooted in creating videos that reflect both music and lyrics through several steps:

• Common Representation Space: Utilizing the representational capabilities of models like CLIP, the approach maps audio and text inputs into a shared space, aligning them with the generated images. This leverages the concept of contrastive learning for multi-modal representation across distinct modalities.
• Variable Length Segmentation: The paper proposes a novel technique for segmenting music based on statistical changes in the audio signal, particularly focusing on musical onset and beat detection to define dynamic intervals. This segmentation is crucial for adapting video content to the variable thematic elements of music.
• Iterative Optimization Process: In contrast to a simple alternation of audio and text prompts, the framework introduces a mechanism to maintain persistent guidance during each segment, allowing for coherent and context-resilient video outputs.
• Time Consistency in Frame Generation: Addressing the issue of temporal coherence in video, the authors implement two techniques: regularization of the GAN's latent vectors between consecutive frames and combining prompts from adjacent frames to sustain narrative consistency.

Results and Implications

The proposed Music2Video framework exhibits promising capabilities in synthesizing artistic videos that are coherently linked to the underlying music and lyrics. By optimizing the fusion of audio and text into the generative process, the method potentially enhances user interactivity, allowing creators to produce videos that are not only visually appealing but also contextually synchronized with the audio track.

This work implies several future directions in the domain of AI-driven creative content generation. The integration of diverse modalities through common representational spaces can extend beyond music videos to broader applications including interactive media and dynamic storytelling. Moreover, advancements in understanding and optimizing multi-modal interactions could lead to further improvements in the fidelity and expressiveness of AI-generated content.

Concluding Remarks

The paper "Music2Video: Automatic Generation of Music Video with Fusion of Audio and Text" presents a substantive contribution to the field of AI-based video generation. Particularly, it provides insights into achieving synchronization and thematic unity between disparate inputs such as music and text, thereby paving the way for more intelligent and versatile multimedia applications. The implications of such a framework highlight the potential of AI to reshape creative processes by offering novel tools for media synthesis and personalization.