# Read, Watch and Scream! Sound Generation from Text and Video

arXiv: https://arxiv.org/abs/2407.05551 · HTML: https://mlsanity.com/paper/2407.05551/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 46 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 30 of 40)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers](https://mlsanity.com/paper/2106.05392/) (2021)
- [Any-to-Any Generation via Composable Diffusion](https://mlsanity.com/paper/2305.11846/) (2023)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model](https://mlsanity.com/paper/2304.13731/) (2023)
- [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)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners](https://mlsanity.com/paper/2402.17723/) (2024)

## Built on by (most cited first, 30 of 48)

- [MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis](https://mlsanity.com/paper/2412.15322/) (2024)
- [JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization](https://mlsanity.com/paper/2503.23377/) (2025)
- [Temporally Aligned Audio for Video with Autoregression](https://mlsanity.com/paper/2409.13689/) (2024)
- [Video-Guided Foley Sound Generation with Multimodal Controls](https://mlsanity.com/paper/2411.17698/) (2024)
- [Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation](https://mlsanity.com/paper/2506.19774/) (2025)
- [UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions](https://mlsanity.com/paper/2511.03334/) (2025)
- [JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation](https://mlsanity.com/paper/2602.19163/) (2026)
- [KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation](https://mlsanity.com/paper/2502.15602/) (2025)
- [Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound](https://mlsanity.com/paper/2408.11915/) (2024)
- [VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation](https://mlsanity.com/paper/2412.10768/) (2024)
- [OmniForcing: Unleashing Real-time Joint Audio-Visual Generation](https://mlsanity.com/paper/2603.11647/) (2026)
- [AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generation](https://mlsanity.com/paper/2508.00733/) (2025)
- [Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition](https://mlsanity.com/paper/2503.06984/) (2025)
- [TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis](https://mlsanity.com/paper/2504.05684/) (2025)
- [Diffusion Classifiers Understand Compositionality, but Conditions Apply](https://mlsanity.com/paper/2505.17955/) (2025)
- [OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation](https://mlsanity.com/paper/2605.12480/) (2026)
- [YingSound: Video-Guided Sound Effects Generation with Multi-modal Chain-of-Thought Controls](https://mlsanity.com/paper/2412.09168/) (2024)
- [TA-V2A: Textually Assisted Video-to-Audio Generation](https://mlsanity.com/paper/2503.10700/) (2025)
- [DeepAudio-V1:Towards Multi-Modal Multi-Stage End-to-End Video to Speech and Audio Generation](https://mlsanity.com/paper/2503.22265/) (2025)
- [SpecMaskFoley: Steering Pretrained Spectral Masked Generative Transformer Toward Synchronized Video-to-audio Synthesis via ControlNet](https://mlsanity.com/paper/2505.16195/) (2025)
- [AudioMoG: Guiding Audio Generation with Mixture-of-Guidance](https://mlsanity.com/paper/2509.23727/) (2025)
- [SoundReactor: Frame-level Online Video-to-Audio Generation](https://mlsanity.com/paper/2510.02110/) (2025)
- [CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction](https://mlsanity.com/paper/2603.00610/) (2026)
- [AVControl: Efficient Framework for Training Audio-Visual Controls](https://mlsanity.com/paper/2603.24793/) (2026)
- [AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment](https://mlsanity.com/paper/2501.18314/) (2025)
- [ReelWave: Multi-Agentic Movie Sound Generation through Multimodal LLM Conversation](https://mlsanity.com/paper/2503.07217/) (2025)
- [Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning](https://mlsanity.com/paper/2505.19614/) (2025)
- [Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation](https://mlsanity.com/paper/2507.02271/) (2025)
- [Schrodinger Audio-Visual Editor: Object-Level Audiovisual Removal](https://mlsanity.com/paper/2512.12875/) (2025)
- [AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer](https://mlsanity.com/paper/2603.15597/) (2026)

## Abstract (arXiv)

Despite the impressive progress of multimodal generative models, video-to-audio generation still suffers from limited performance and limits the flexibility to prioritize sound synthesis for specific objects within the scene. Conversely, text-to-audio generation methods generate high-quality audio but pose challenges in ensuring comprehensive scene depiction and time-varying control. To tackle these challenges, we propose a novel video-and-text-to-audio generation method, called ours, where video serves as a conditional control for a text-to-audio generation model. Especially, our method estimates the structural information of sound (namely, energy) from the video while receiving key content cues from a user prompt. We employ a well-performing text-to-audio model to consolidate the video control, which is much more efficient for training multimodal diffusion models with massive triplet-paired (audio-video-text) data. In addition, by separating the generative components of audio, it becomes a more flexible system that allows users to freely adjust the energy, surrounding environment, and primary sound source according to their preferences. Experimental results demonstrate that our method shows superiority in terms of quality, controllability, and training efficiency. Code and demo are available at https://naver-ai.github.io/rewas.