arXiv:2404.16305v1 Announce Type: new
Abstract: Existing works have made strides in video generation, but the lack of sound effects (SFX) and background music (BGM) hinders a complete and immersive viewer experience. We introduce a novel semantically consistent v ideo-to-audio generation framework, namely SVA, which automatically generates audio semantically consistent with the given video content. The framework harnesses the power of multimodal large language model (MLLM) to understand video semantics from a key frame and generate creative audio schemes, which are then utilized as prompts for text-to-audio models, resulting in video-to-audio generation with natural language as an interface. We show the satisfactory performance of SVA through case study and discuss the limitations along with the future research direction. The project page is available at https://huiz-a.github.io/audio4video.github.io/.
Improving the Immersive Experience with Video-to-Audio Generation
In the field of multimedia information systems, the combination of audio and visual elements plays a crucial role in creating an immersive viewer experience. While existing works have made significant strides in video generation, there has been a lack of attention to the inclusion of sound effects (SFX) and background music (BGM) in the generated videos. This omission hinders the creation of a complete and truly immersive viewer experience.
To address this limitation, a novel framework called SVA (Semantically-consistent Video-to-Audio generation) has been introduced. The primary objective of SVA is to automatically generate audio that is semantically consistent with the given video content. By harnessing the power of multimodal large language models (MLLM), SVA is able to understand the semantics of a video from its key frame and generate creative audio schemes that correspond to it.
The use of multimodal language models is significant in highlighting the multi-disciplinary nature of this research. It brings together concepts from natural language processing, computer vision, and audio processing to create an integrated framework that addresses a gap in the existing video generation techniques.
SVA makes use of prompts generated by the MLLM to drive text-to-audio models. These text-to-audio models then generate the final audio that is synchronized with the video content. The natural language interface provided by the prompts allows for intuitive control over the audio generation process.
The successful implementation of SVA has been demonstrated through a case study, which showcases the satisfactory performance of the framework. By generating audio that is semantically consistent with the video, SVA enhances the overall viewer experience, making it more immersive and engaging.
Looking ahead, the limitations and future research directions of the SVA framework need to be explored. For instance, how can the generation of audio be further enhanced to capture more fine-grained details of the video content? Additionally, the integration of SVA with emerging technologies such as augmented reality (AR) and virtual reality (VR) could open up new possibilities for creating highly immersive multimedia experiences.
In conclusion, the introduction of the SVA framework represents a significant advancement in the field of multimedia information systems. By automatically generating semantically consistent audio for videos, SVA contributes to the creation of more immersive and engaging viewer experiences. Its multi-disciplinary nature, combining concepts from natural language processing, computer vision, and audio processing, highlights the importance of integrating multiple domains for the advancement of multimedia technologies.
You can learn more about the SVA framework and access the project page here.