VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling (2406.04321v3)

Abstract: In this work, we systematically study music generation conditioned solely on the video. First, we present a large-scale dataset comprising 360K video-music pairs, including various genres such as movie trailers, advertisements, and documentaries. Furthermore, we propose VidMuse, a simple framework for generating music aligned with video inputs. VidMuse stands out by producing high-fidelity music that is both acoustically and semantically aligned with the video. By incorporating local and global visual cues, VidMuse enables the creation of musically coherent audio tracks that consistently match the video content through Long-Short-Term modeling. Through extensive experiments, VidMuse outperforms existing models in terms of audio quality, diversity, and audio-visual alignment. The code and datasets are available at https://vidmuse.github.io/.

• The paper presents a framework that leverages a Long-Short-Term Visual Module and a Transformer-based Music Token Decoder to ensure coherent audio generation from video input.
• It introduces a comprehensive V2M dataset with approximately 190K diverse video-music pairs for robust training and evaluation.
• Benchmarking reveals superior performance in audio quality, diversity, and audio-visual alignment, setting a new standard in autonomous video-to-music generation.

Overview of VidMuse: A Video-to-Music Generation Framework

The paper "VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling" presents a comprehensive exploration into the field of video-conditioned music generation. The authors systematically address the challenges involved in creating music that aligns both acoustically and semantically with video content. With the rise of social media platforms and the increasing demand for engaging audiovisual content, the automated generation of music from video inputs presents a significant area of interest.

Key Contributions

The authors' contributions can be summarized as follows:

1. V2M Dataset Construction: The research introduces a large-scale dataset, V2M, comprising approximately 190,000 video-music pairs. These pairs cover various genres such as movie trailers, advertisements, documentaries, and vlogs. The dataset is meticulously constructed through a multi-step pipeline to ensure the quality and diversity of the data. This dataset serves as a critical resource for training and evaluating video-to-music generation models.
2. VidMuse Framework: VidMuse is proposed as a simple yet effective framework for generating music from videos. By incorporating both local and global visual cues through Long-Short-Term modeling, VidMuse can produce musically coherent tracks that align with video content. The framework employs a Long-Short-Term Visual Module to capture spatial-temporal representations of videos, and a Transformer-based Music Token Decoder converts these insights into music tokens.
3. Benchmarking and Evaluation: The framework is evaluated against several state-of-the-art models using a series of subjective and objective metrics. VidMuse demonstrates superior performance in terms of audio quality, diversity, and audio-visual alignment, thereby showcasing its efficacy in generating contextually relevant music.

Methodological Insights

VidMuse leverages two key components for its core processing: the Long-Short-Term Visual Module and the Music Token Decoder. The former is responsible for extracting both the short-term, fine-grained visual features, and long-term, video-level context, thereby ensuring the generated music is both detailed and coherent. The integration of the Long-Short-Term Visual Module into the music generation process allows VidMuse to utilize comprehensive video understanding, which contributes significantly to its success in aligning music with video content.

The Music Token Decoder, based on a transformer architecture, translates visual features into musical tokens, effectively repurposing techniques well-established in NLP for audio applications. This process is guided by a carefully constructed dataset, ensuring the generation model is robust against various video genres.

Implications and Future Work

VidMuse marks a significant advancement in the autonomous generation of music from video content. Its practical implications are notable, offering the potential to enhance multimedia production processes across diverse fields such as digital marketing, film production, and content creation for social media. Theoretically, VidMuse contributes to the understanding and development of audio-visual alignment and multi-modal learning.

Future developments may explore enhancing the quality and diversity of generated music by integrating more sophisticated audio codecs or experimenting with alternative machine learning architectures. Additionally, exploring the integration of higher fidelity video representations could further bolster the alignment quality between video and audio outputs. Another avenue of exploration could involve real-time video-to-music applications, optimizing VidMuse for live and interactive multimedia experiences.

In conclusion, VidMuse not only adequately addresses the challenges associated with video-to-music generation but also sets a foundation for future research and practical applications in this burgeoning field. The dataset and methodologies introduced have the potential to drive substantial progress in multi-modal AI research, paving the way for more seamless integration of computational creativity into daily media consumption and production.