Seeing-and-Hearing: An Innovative Project in Visual and Audio Generation
Overview of the Project
The Seeing-and-Hearing project aims to bridge the gap between video and audio creation using an advanced method called diffusion latent aligners. This project stands out by enabling the creation and integration of both visual and audio content through a versatile framework. This framework is capable of handling four distinct tasks: joint video-audio generation, transforming video into audio (V2A), converting audio into video (A2V), and translating images into audio (I2A).
Methodology
The core of the Seeing-and-Hearing project lies in its use of a multimodal binder, which is crucial for linking separate generative models designed originally for a single output, like video or audio alone. By leveraging a pre-trained model called ImageBind, the project aims to establish a connection that supports bidirectional and joint video/audio generation. This innovative approach allows for the integration and enhancement of existing models without the need to rebuild them from scratch.
Abstract
Creating video and audio content is essential for industries like filmmaking and other professional domains. Historically, techniques for generating video and audio have evolved separately, which poses challenges for practical application in industries. The Seeing-and-Hearing project addresses this by developing an optimization-based framework that facilitates both visual-audio cross-generation and combined visual-audio creation. The project utilizes strong existing models but connects them through a shared latent space, enabling superior results without excessive re-development efforts.
Current Progress and Future Directions
The project has successfully released open-source codes for V2A (video-to-audio) tasks and evaluation processes. The team is currently working on making the codes for joint video-audio generation, audio-to-video, and image-to-audio tasks available as well.
Technical Setup
To get started with the Seeing-and-Hearing project, users can follow specific installation instructions to set up their environment. This includes downloading necessary checkpoints from platforms like Hugging Face and official repositories, and setting up the codebase using package managers like conda and pip.
Research Impact
The Seeing-and-Hearing project directly impacts how video and audio can be created conjointly, paving the way for smoother integration of multimedia content in various applications. Its methodology using diffusion latent aligners and multimodal binders is at the forefront of research, potentially transforming how professionals approach content creation in open domains.
Contact and Citation
If you are interested in learning more or contributing ideas, you can reach out to the project team members via their emails. For academic purposes, you should cite the project's work available in the CVPR 2024 proceedings.
Conclusion
The Seeing-and-Hearing project showcases an advanced approach to synchronizing video and audio generations. Through its unique framework and use of pre-trained models, it opens new possibilities for seamless multimedia content development, promising enhanced creativity and efficiency in the industry.