# Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis

arXiv: https://arxiv.org/abs/2103.14201 · HTML: https://mlsanity.com/paper/2103.14201/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [Deep Cross-Modal Audio-Visual Generation](https://mlsanity.com/paper/1704.08292/) (2017)
- [2.5D Visual Sound](https://mlsanity.com/paper/1812.04204/) (2018)
- [Scene-Aware Audio for 360textdegree Videos](https://mlsanity.com/paper/1805.04792/) (2018)
- [IR-GAN: Room Impulse Response Generator for Far-field Speech Recognition](https://mlsanity.com/paper/2010.13219/) (2020)

## Built on by (most cited first, 30 of 34)

- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis](https://mlsanity.com/paper/2302.02088/) (2023)
- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [Visual Acoustic Matching](https://mlsanity.com/paper/2202.06875/) (2022)
- [FAST-RIR: Fast neural diffuse room impulse response generator](https://mlsanity.com/paper/2110.04057/) (2021)
- [Novel-View Acoustic Synthesis](https://mlsanity.com/paper/2301.08730/) (2023)
- [Learning Audio-Visual Dereverberation](https://mlsanity.com/paper/2106.07732/) (2021)
- [MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes](https://mlsanity.com/paper/2205.09248/) (2022)
- [Measuring Acoustics with Collaborative Multiple Agents](https://mlsanity.com/paper/2310.05368/) (2023)
- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark](https://mlsanity.com/paper/2403.18821/) (2024)
- [Sound Localization from Motion: Jointly Learning Sound Direction and Camera Rotation](https://mlsanity.com/paper/2303.11329/) (2023)
- [AdVerb: Visually Guided Audio Dereverberation](https://mlsanity.com/paper/2308.12370/) (2023)
- [Self-Supervised Visual Acoustic Matching](https://mlsanity.com/paper/2307.15064/) (2023)
- [SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos](https://mlsanity.com/paper/2404.05206/) (2024)
- [Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes](https://mlsanity.com/paper/2302.02809/) (2023)
- [NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields](https://mlsanity.com/paper/2405.18213/) (2024)
- [Self-Supervised Audio-Visual Soundscape Stylization](https://mlsanity.com/paper/2409.14340/) (2024)
- [Hearing Anywhere in Any Environment](https://mlsanity.com/paper/2504.10746/) (2025)
- [Differentiable Room Acoustic Rendering with Multi-View Vision Priors](https://mlsanity.com/paper/2504.21847/) (2025)
- [How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes](https://mlsanity.com/paper/2508.02905/) (2025)
- [Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning](https://mlsanity.com/paper/2304.05600/) (2023)
- [DIFFRENT: A Diffusion Model for Recording Environment Transfer of Speech](https://mlsanity.com/paper/2401.08102/) (2024)
- [SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding](https://mlsanity.com/paper/2504.05576/) (2025)
- [ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling](https://mlsanity.com/paper/2404.16216/) (2024)
- [Enhancing XR Auditory Realism via Multimodal Scene-Aware Acoustic Rendering](https://mlsanity.com/paper/2511.11930/) (2025)
- [Few-shot Acoustic Synthesis with Multimodal Flow Matching](https://mlsanity.com/paper/2603.19176/) (2026)
- [One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In Any Room/ Concert Hall](https://mlsanity.com/paper/2210.15750/) (2022)
- [Mutual Learning for Acoustic Matching and Dereverberation via Visual Scene-driven Diffusion](https://mlsanity.com/paper/2407.10373/) (2024)

## Abstract (arXiv)

Measuring the acoustic characteristics of a space is often done by capturing its impulse response (IR), a representation of how a full-range stimulus sound excites it. This work generates an IR from a single image, which can then be applied to other signals using convolution, simulating the reverberant characteristics of the space shown in the image. Recording these IRs is both time-intensive and expensive, and often infeasible for inaccessible locations. We use an end-to-end neural network architecture to generate plausible audio impulse responses from single images of acoustic environments. We evaluate our method both by comparisons to ground truth data and by human expert evaluation. We demonstrate our approach by generating plausible impulse responses from diverse settings and formats including well known places, musical halls, rooms in paintings, images from animations and computer games, synthetic environments generated from text, panoramic images, and video conference backgrounds.