Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows
Abstract
Coordinated audio generation based on video inputs typically requires a strict audio-visual (AV) alignment, where both semantics and rhythmics of the generated audio segments shall correspond to those in the video frames. Previous studies leverage a two-stage design where the AV encoders are firstly aligned via contrastive learning, then the encoded video representations guide the audio generation process. We observe that both contrastive learning and global video guidance are effective in aligning overall AV semantics while limiting temporally rhythmic synchronization. In this work, we propose FoleyFlow to first align unimodal AV encoders via masked modeling training, where the masked audio segments are recovered under the guidance of the corresponding video segments. After training, the AV encoders which are separately pretrained using only unimodal data are aligned with semantic and rhythmic consistency. Then, we develop a dynamic conditional flow for the final audio generation. Built upon the efficient velocity flow generation framework, our dynamic conditional flow utilizes temporally varying video features as the dynamic condition to guide corresponding audio segment generations. To this end, we extract coherent semantic and rhythmic representations during masked AV alignment, and use this representation of video segments to guide audio generation temporally. Our audio results are evaluated on the standard benchmarks and largely surpass existing results under several metrics. The superior performance indicates that FoleyFlow is effective in generating coordinated audios that are both semantically and rhythmically coherent to various video sequences.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
FoleyFlow presents a novel approach to coordinated video-to-audio generation, significantly advancing the field by introducing masked audio-visual alignment and dynamic conditional flows. The paper's contributions are substantial, showcasing a promising direction for future research in audio-visual synthesis.
Methodology
The methodology proposed in FoleyFlow is innovative, particularly in its approach to aligning unimodal audio and video encoders through masked modeling training. This technique allows for the recovery of masked audio segments under the guidance of corresponding video segments, which is a departure from traditional contrastive learning methods. The introduction of dynamic conditional flows to guide audio generation based on temporally varying video features is a significant advancement, demonstrating a thoughtful integration of temporal dynamics into the audio generation process.
Results & evaluation
The experiments conducted in this paper are robust, utilizing standard benchmarks to evaluate the performance of the proposed method. The results indicate a substantial improvement over existing methods across various metrics, suggesting that the authors have effectively validated their approach. However, the paper could benefit from a more detailed discussion of the datasets used and the specific metrics employed for evaluation.
Reproducibility
The paper lacks sufficient implementation details that would facilitate reproducibility. While the methodology is described, there is no mention of code availability or specific configurations for the experiments, which could hinder other researchers from replicating the results.
Limitations
One limitation is the reliance on the quality of the video input, as the method's performance may degrade with low-quality or poorly aligned video data. Additionally, the paper does not address potential challenges in real-time applications, which could limit the practical applicability of the proposed method.
Broader impact
The implications of this research are significant, particularly in fields such as film production, gaming, and virtual reality, where synchronized audio-visual content is crucial. The ability to generate coherent audio that aligns with video content can enhance user experience and open new avenues for creative applications in multimedia. FoleyFlow presents a novel approach to coordinated video-to-audio generation, significantly advancing the field by introducing masked audio-visual alignment and dynamic conditional flows. The paper's contributions are substantial, showcasing a promising direction for future research in audio-visual synthesis.
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