- The paper introduces VTA-LDM, a novel framework that employs latent diffusion models and vision encoders to synthesize semantically and temporally aligned audio from silent videos.
- It integrates auxiliary embeddings, including textual, positional, and optical flow features, to enhance context and improve alignment metrics.
- Experimental results demonstrate that VTA-LDM outperforms baselines on metrics such as FAD, IS, FD, and KL divergence, confirming its robust audio synthesis capabilities.
Video-to-Audio Generation with Hidden Alignment
The paper at hand investigates the domain of video-to-audio (VTA) generation, addressing the challenge of synthesizing semantically and temporally aligned audio based on silent video inputs. The authors propose a novel framework named VTA-LDM, which leverages latent diffusion models (LDMs) and various vision encoders to accomplish this task.
Key Aspects of the Framework
The authors emphasize three primary components critical to the performance of their VTA-LDM model: vision encoders, auxiliary embeddings, and data augmentation techniques.
Vision Encoders
The choice of vision encoder is paramount to the model's ability to extract and interpret relevant visual features from the input video. Different vision encoders, such as Clip4Clip, ImageBind, LanguageBind, V-JEPA, ViViT, and CAVP, were evaluated. The authors found that Clip4Clip, based on the pre-trained CLIP model, performs optimally in capturing both semantic and temporal information, thus leading to superior audio generation results. The evaluation metrics, including Fréchet Audio Distance (FAD), Inception Score (IS), Fréchet Distance (FD), and Kullback-Leibler (KL) divergence, illustrate significant improvements over alternative vision encoders.
Auxiliary Embeddings
Auxiliary embeddings were investigated to provide additional context to the generation process. This includes textual information, positional embeddings, and optical flow features. The integration of these embeddings enhances the model's performance, as evidenced by improvements in semantic and temporal alignment metrics. Notably, combining auxiliary text embeddings with visual features resulted in a notable increase in the Inception Score, demonstrating the added value of context-aware conditions in the generated audio.
Data Augmentation
Data augmentation techniques, such as data cleaning, concatenation of different audio events, and pretraining on extensive datasets, were explored to improve the model's generalization capabilities. The use of a pre-trained audio latent representation from AudioLDM and additional video-audio data from YouTube and WavCaps datasets significantly enhanced the model's robustness. The experiments suggest that cleaner, more extensive datasets contribute to better alignment and overall audio quality.
Experimental Results
The experiments showcase the efficacy of the proposed VTA-LDM framework against existing baselines like IM2WAV and Diff-Foley. The VTA-LDM model consistently outperforms these baselines in metrics such as FAD, IS, FD, KL divergence, and AV-Align, indicating superior semantic relevance and synchronization of the generated audio with the input video.
Implications and Future Directions
The findings from this research have several noteworthy implications. Practically, the improved ability to generate realistic and synchronized audio from silent video inputs can significantly enhance experiences in various multimedia applications, such as virtual reality, post-production in filmmaking, and accessibility tools. Theoretically, this work underscores the importance of multi-modal learning and the potential of diffusion-based models in generative tasks.
Looking forward, the authors acknowledge the limitations of training on datasets like VGGSound, which primarily contain single audio events. Future research should focus on constructing more diverse and complex datasets to train and evaluate models. Expanding the model's capability to handle open-domain video content and addressing the ethical concerns around potential misuse of this technology will be essential. There is also scope for exploring more sophisticated vision encoders and augmentations to further enhance model performance.
In conclusion, this paper provides valuable insights into the VTA generation paradigm and sets a substantial foundation for future advancements in audio-visual generative modeling. The proposed VTA-LDM framework, with its emphasis on vision encoders, auxiliary embeddings, and data augmentation, represents a significant step toward generating more accurate and realistic video-conditioned audio content. This research paves the way for continued exploration and refinement in the domain of synchronized audio-visual content generation.