Generating Visually Aligned Sound from Videos (2008.00820v1)

Abstract: We focus on the task of generating sound from natural videos, and the sound should be both temporally and content-wise aligned with visual signals. This task is extremely challenging because some sounds generated \emph{outside} a camera can not be inferred from video content. The model may be forced to learn an incorrect mapping between visual content and these irrelevant sounds. To address this challenge, we propose a framework named REGNET. In this framework, we first extract appearance and motion features from video frames to better distinguish the object that emits sound from complex background information. We then introduce an innovative audio forwarding regularizer that directly considers the real sound as input and outputs bottlenecked sound features. Using both visual and bottlenecked sound features for sound prediction during training provides stronger supervision for the sound prediction. The audio forwarding regularizer can control the irrelevant sound component and thus prevent the model from learning an incorrect mapping between video frames and sound emitted by the object that is out of the screen. During testing, the audio forwarding regularizer is removed to ensure that REGNET can produce purely aligned sound only from visual features. Extensive evaluations based on Amazon Mechanical Turk demonstrate that our method significantly improves both temporal and content-wise alignment. Remarkably, our generated sound can fool the human with a 68.12% success rate. Code and pre-trained models are publicly available at https://github.com/PeihaoChen/regnet

• The paper introduces RegNet, a framework to synthesize sound from videos that are visually aligned and robust to irrelevant background noise.
• RegNet uses an audio forwarding regularizer during training, which leverages real sound to guide the model in learning correct visual-to-audio mappings without interfering with predictions during testing.
• Evaluations show RegNet generates sound with superior temporal and content alignment, achieving a 68.12% human false positive rate in perception tests, with applications in multimedia editing and accessibility.

Generating Visually Aligned Sound from Videos: An Analysis of RegNet

In "Generating Visually Aligned Sound from Videos," the authors focus on a pivotal challenge in multimedia and machine learning: synthesizing sound from natural video inputs while ensuring temporal and content consistency with visual signals. This task is complex, given the non-trivial coupling between visual events and their corresponding audio components, as well as the presence of irrelevant sounds during video capturing that can mislead a model into incorrect associations.

To tackle this, the authors propose RegNet, a framework that integrates appearance and motion feature extraction from video frames along with an innovative module known as the audio forwarding regularizer. This design explicitly addresses the problem of irrelevant sound components by allowing the model to better focus on sound elements directly related to the visual input. The inclusion of this regularizer is a significant advancement as it bridges the gap in mapping features from the visual domain to the corresponding audio outputs, thereby enhancing the temporal and content-wise alignment of the generated sound.

Framework and Methodology

RegNet is structured to extract and unify temporal features from video frames, encapsulating both motion and static properties of the visual data. The core of the framework leverages a BN-Inception model for feature extraction. Importantly, the introduced audio forwarding regularizer takes real sound as an input during the training phase to produce bottlenecked sound features, which guide the model in learning correct mappings without the interference of extraneous audio components.

During training, both the visual features and these bottlenecked sound inputs are utilized, balancing visual and audio facets directly related to the scene at hand. However, this regularizer is excluded during testing to ensure that generated sounds derive solely from visual cues, ensuring that predictions are devoid of artifact sounds from irrelevant sources.

Evaluation and results indicate that the sound generated by RegNet not only demonstrates superior alignment in both the temporal and content dimensions but also achieves a significant human false positive rate in realistic perception tests, fooling subjects 68.12% of the time.

Implications and Future Directions

The work presents substantial evidence for the effectiveness of the audio forwarding regularizer in overcoming challenges tied to irrelevant information in visual-sound generation tasks. By effectively controlling the learned mappings, the authors provide a pathway toward more reliable and consistent audio generation models in multimedia applications such as video editing, creating soundscapes for silent films, and aiding visually impaired individuals.

Future research might explore extending this framework to adapt to dynamic distribution changes in real-world scenarios or expand its generalization capability across diverse video categories. Additionally, integrating this approach with larger-scale models or transformers could enhance its robustness and performance across broader multimedia tasks.

The significance of RegNet lies not only in enhancing sound generation fidelity but also in its potential to integrate with and enhance other multimedia processing fields where synchrony between modalities is paramount.