- The paper introduces V-AURA, an autoregressive model that enhances audio-video alignment by directly encoding waveforms without lossy conversions.
- It employs a high-framerate visual feature extractor and cross-modal fusion to capture fine-grained motion features for improved audio generation.
- Evaluation on the VisualSound benchmark demonstrates significant improvements in sync scores and reduced hallucination compared to diffusion-based methods.
Temporally Aligned Audio for Video with Autoregression: A Detailed Examination of V-AURA
The paper "Temporally Aligned Audio for Video with Autoregression" introduces V-AURA, an autoregressive model designed to generate audio from video with heightened temporal alignment and semantic relevance. This contribution stands in contrast to existing methods which tend to rely heavily on diffusion models and rectified flow matching that involve added complexity. The development of V-AURA highlights the importance of utilizing autoregressive models to achieve optimal synchronization between audio and corresponding visual events.
Core Methodology
V-AURA innovates by employing a high-framerate visual feature extractor alongside a cross-modal fusion strategy to extract and align fine-grained motion features with audio. Unlike diffusion models which convert audio to mel-spectrograms—a process that inherently involves loss due to frequency filtering—V-AURA encodes waveforms directly into discrete token sequences without reducing them to image-space. This avoids the lossy conversions necessary in diffusion methods and retains the detailed auditory information to ensure precision in audio generation.
The model relies on a visual feature extractor, Segment AVCLIP, for capturing high-precision visual features that correlate with auditory events. This, combined with a bespoke autoregressive approach that emphasizes temporal fidelity by aligning the tokenized audio and visual cues, allows it to outperform existing methods in maintaining synchronicity in the generated outputs.
Dataset and Evaluation
A significant contribution of this work is the introduction of VisualSound, a benchmark dataset for video-to-audio tasks with high audio-visual relevance. Derived from the more generic VGGSound dataset, VisualSound is carefully curated to filter out samples where auditory and visual events do not align. This curation notably improves training efficiency by cutting down irrelevant data, which in turn reduces the chance of generating hallucinations within the audio outputs.
The model's performance is assessed against state-of-the-art methods on several datasets, present and newly introduced, showcasing significant improvements in temporal alignment metrics, specifically the Sync score, and the audio-visual relevance, measured via IB and KLD.
Implications and Future Directions
The results indicate that autoregressive models like V-AURA provide a compelling alternative to current diffusion and RFM-based models by simplifying the training and inference processes while simultaneously enhancing the alignment of multimodal outputs. The introduction of VisualSound as a dedicated multimodal dataset underscores the potential gain from carefully curated training data, especially when assessing audio-visual correlation.
Future work in this domain can focus on further refining the autoregressive approach to model synchronous features in varying contexts and environments, potentially extending this to other modalities like text-to-audio or integrating a broader range of environmental sounds. Additionally, exploring how similar autoregressive strategies could be used to refine generative capabilities in other fields holds promising pathways for ongoing AI research.
This research sets a precedent for improving temporal alignment and semantic relevance in video-to-audio models, urging a revisitation of autoregressive frameworks as viable candidates for advanced audiovisual tasks.