Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation (2506.19774v1)

Abstract: We propose Kling-Foley, a large-scale multimodal Video-to-Audio generation model that synthesizes high-quality audio synchronized with video content. In Kling-Foley, we introduce multimodal diffusion transformers to model the interactions between video, audio, and text modalities, and combine it with a visual semantic representation module and an audio-visual synchronization module to enhance alignment capabilities. Specifically, these modules align video conditions with latent audio elements at the frame level, thereby improving semantic alignment and audio-visual synchronization. Together with text conditions, this integrated approach enables precise generation of video-matching sound effects. In addition, we propose a universal latent audio codec that can achieve high-quality modeling in various scenarios such as sound effects, speech, singing, and music. We employ a stereo rendering method that imbues synthesized audio with a spatial presence. At the same time, in order to make up for the incomplete types and annotations of the open-source benchmark, we also open-source an industrial-level benchmark Kling-Audio-Eval. Our experiments show that Kling-Foley trained with the flow matching objective achieves new audio-visual SOTA performance among public models in terms of distribution matching, semantic alignment, temporal alignment and audio quality.

• The paper introduces a multimodal diffusion transformer that synchronizes video, audio, and text modalities for precise latent audio generation.
• The methodology features a universal latent audio codec and advanced synchronization modules, resulting in lower FD, KL scores and improved SDR metrics.
• Empirical results demonstrate state-of-the-art performance in semantic and temporal alignment, enhancing immersive video-to-audio synthesis.

An Expert Analysis of Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation

The paper "Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation" introduces a sophisticated multimodal model aimed at enriching the domain of Video-to-Audio (V2A) generation. The model uses a multimodal diffusion transformer approach to synchronize and enhance audio generation in conjunction with video content. This essay provides a detailed exploration of the methodologies, results, and future implications of Kling-Foley as outlined in the paper.

Methodological Innovations

Kling-Foley represents a significant advancement in the integration of video, audio, and text modalities. At the core, the model utilizes a multimodal diffusion transformer framework, which is coupled with a visual semantic representation module and an audio-visual synchronization module. These modules are pivotal in aligning video conditions with latent audio features at the frame level, thereby facilitating seamless semantic and temporal synchronization. The inclusion of text-based conditions further refines the generation of sound effects aligned with visual content.

Interestingly, Kling-Foley innovates through its universal latent audio codec, adept at handling diversified audio scenarios, including sound effects, speech, singing, and music. This codec's capability to imbue audio with spatial presence through stereo rendering markedly enhances the immersive experience of audio content, setting the model apart from contemporary solutions.

Empirical Outcomes

Empirically, Kling-Foley demonstrates superior performance benchmarks when assessed on various dimensions critical to V2A generation. The model achieves state-of-the-art (SOTA) results in distribution matching, semantic alignment, temporal alignment, and overall audio quality. With specific focus on distribution matching, the model attained lower Fréchet Distance (FD) and Kullback-Leibler divergence (KL) scores, indicating highly similar feature distributions between generated and ground-truth audio. Furthermore, the model achieved an enhanced ImageBind score for video-audio semantic alignment and minimized DeSync errors, indicative of effective synchronization between audio and video streams.

In the domain of audio quality, assessed through metrics like SDR and Mel-Cepstral Distortion, Kling-Foley consistently outperformed existing methods, showcasing its robust capability in generating high-fidelity audio content.

Dataset and Evaluation Enhancements

Kling-Foley also addresses certain gaps in available resources by introducing Kling-Audio-Eval, a new benchmark designed to comprehensively evaluate multimodal generation models. This dataset includes 20,935 annotated samples across diverse acoustic and visual scenarios, enriching the evaluation landscape with synchronized video, audio, and text annotations.

Theoretical and Practical Implications

The implications of Kling-Foley are multi-faceted. Theoretically, it provides a framework for advancing the understanding and integration of multimodal data in generative models, setting a new precedent in the field of audio-visual interaction. The methodology can guide future research in creating more sophisticated models that handle contextual and temporal nuances of multimodal data more effectively.

Practically, the model has direct applications in automating and enhancing the production of audio content in media, gaming, and interactive applications. It reduces the reliance on manual video dubbing while enhancing the realism and immersion of audio-visual content, offering significant cost and time efficiencies in production pipelines.

Future Prospects

Looking forward, the research highlights avenues for further exploration, particularly in extending the model's scalability to accommodate longer video sequences without synchronization drift. Another potential area for development is improving the modeling of complex physical audio scenarios involving multiple interactive sound sources. Such advancements would further bolster the model's utility in real-time applications and complex soundscapes.

In conclusion, Kling-Foley emerges as a pivotal innovation in the domain of V2A generation, offering a comprehensive, multi-faceted approach to high-fidelity audio synthesis synchronized with video content. The research paves the way for future explorations into more intricate and scalable integration of multimodal frameworks.