arXiv:2502.03897v1 Announce Type: new
Abstract: As a natural multimodal content, audible video delivers an immersive sensory experience. Consequently, audio-video generation systems have substantial potential. However, existing diffusion-based studies mainly employ relatively independent modules for generating each modality, which lack exploration of shared-weight generative modules. This approach may under-use the intrinsic correlations between audio and visual modalities, potentially resulting in sub-optimal generation quality. To address this, we propose UniForm, a unified diffusion transformer designed to enhance cross-modal consistency. By concatenating auditory and visual information, UniForm learns to generate audio and video simultaneously within a unified latent space, facilitating the creation of high-quality and well-aligned audio-visual pairs. Extensive experiments demonstrate the superior performance of our method in joint audio-video generation, audio-guided video generation, and video-guided audio generation tasks. Our demos are available at https://uniform-t2av.github.io/.
Analysis of UniForm: A Unified Diffusion Transformer for Multimodal Content Generation
In this article, the authors propose UniForm, a unified diffusion transformer model for enhancing cross-modal consistency in audio-video generation systems. The goal is to generate high-quality and well-aligned audio-visual pairs by exploiting the intrinsic correlations between audio and visual modalities.
Existing studies in diffusion-based audio-video generation have mostly focused on generating each modality independently. However, this approach may not fully exploit the interdependence and correlations between audio and visual information, leading to sub-optimal generation quality. UniForm addresses this limitation by creating a unified latent space that combines auditory and visual information.
The key idea behind UniForm is to concatenate auditory and visual information and use it as input to the diffusion transformer model. By doing so, the model learns to generate audio and video simultaneously, leveraging the shared weight generative modules. This approach promotes better alignment between audio and visual modalities, resulting in improved quality of the generated content.
The significance of this research lies in its multi-disciplinary nature. It combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The integration of audio and visual modalities is a central theme in these fields, and UniForm contributes to the advancement of this integration.
Furthermore, UniForm has implications for various applications. It can be used in joint audio-video generation, where both audio and video are generated together. This can be useful in the creation of immersive and interactive multimedia content. Additionally, UniForm can also be used in audio-guided video generation and video-guided audio generation tasks, where one modality guides the generation of the other. These applications have potential in areas like virtual reality, where realistic audio-visual experiences are crucial.
Overall, UniForm presents a novel approach to audio-video generation by utilizing a unified diffusion transformer model. Its focus on cross-modal consistency and the exploration of shared-weight generative modules sets it apart from existing studies. The demonstrated superior performance in various tasks showcases the effectiveness of UniForm in generating high-quality and well-aligned audio-visual pairs. This research contributes to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities by advancing the understanding and techniques for integrating audio and visual modalities in a unified manner.