# Generating Visually Aligned Sound from Videos

arXiv: https://arxiv.org/abs/2008.00820 · HTML: https://mlsanity.com/paper/2008.00820/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 30 of 67)

- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [Taming Visually Guided Sound Generation](https://mlsanity.com/paper/2110.08791/) (2021)
- [Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2306.17203/) (2023)
- [MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis](https://mlsanity.com/paper/2412.15322/) (2024)
- [FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds](https://mlsanity.com/paper/2407.01494/) (2024)
- [V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Models](https://mlsanity.com/paper/2308.09300/) (2023)
- [Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching](https://mlsanity.com/paper/2406.00320/) (2024)
- [Temporally Aligned Audio for Video with Autoregression](https://mlsanity.com/paper/2409.13689/) (2024)
- [Learning in Audio-visual Context: A Review, Analysis, and New Perspective](https://mlsanity.com/paper/2208.09579/) (2022)
- [Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment](https://mlsanity.com/paper/2303.17490/) (2023)
- [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://mlsanity.com/paper/2306.00956/) (2023)
- [Masked Motion Encoding for Self-Supervised Video Representation Learning](https://mlsanity.com/paper/2210.06096/) (2022)
- [MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World](https://mlsanity.com/paper/2401.08577/) (2024)
- [ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency](https://mlsanity.com/paper/2106.02342/) (2021)
- [Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes](https://mlsanity.com/paper/2203.13412/) (2022)
- [Unsupervised Sound Localization via Iterative Contrastive Learning](https://mlsanity.com/paper/2104.00315/) (2021)
- [Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity](https://mlsanity.com/paper/2407.10387/) (2024)
- [Geometry-Aware Multi-Task Learning for Binaural Audio Generation from Video](https://mlsanity.com/paper/2111.10882/) (2021)
- [Vision+X: A Survey on Multimodal Learning in the Light of Data](https://mlsanity.com/paper/2210.02884/) (2022)
- [Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos](https://mlsanity.com/paper/2303.16897/) (2023)
- [Action2Sound: Ambient-Aware Generation of Action Sounds from Egocentric Videos](https://mlsanity.com/paper/2406.09272/) (2024)
- [FoleyGAN: Visually Guided Generative Adversarial Network-Based Synchronous Sound Generation in Silent Videos](https://mlsanity.com/paper/2107.09262/) (2021)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [A Simple but Strong Baseline for Sounding Video Generation: Effective Adaptation of Audio and Video Diffusion Models for Joint Generation](https://mlsanity.com/paper/2409.17550/) (2024)
- [V2Meow: Meowing to the Visual Beat via Video-to-Music Generation](https://mlsanity.com/paper/2305.06594/) (2023)
- [Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound](https://mlsanity.com/paper/2408.11915/) (2024)
- [Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation](https://mlsanity.com/paper/2303.16541/) (2023)
- [VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos](https://mlsanity.com/paper/2409.07450/) (2024)
- [Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation](https://mlsanity.com/paper/2105.00708/) (2021)
- [MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization](https://mlsanity.com/paper/2410.12957/) (2024)

## Abstract (arXiv)

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 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