# AudioScenic: Audio-Driven Video Scene Editing

arXiv: https://arxiv.org/abs/2404.16581 · HTML: https://mlsanity.com/paper/2404.16581/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 50 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 30 of 36)

- [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)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [TokenFlow: Consistent Diffusion Features for Consistent Video Editing](https://mlsanity.com/paper/2307.10373/) (2023)
- [Video-P2P: Video Editing with Cross-attention Control](https://mlsanity.com/paper/2303.04761/) (2023)
- [Text2LIVE: Text-Driven Layered Image and Video Editing](https://mlsanity.com/paper/2204.02491/) (2022)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://mlsanity.com/paper/2306.07954/) (2023)
- [Dreamix: Video Diffusion Models are General Video Editors](https://mlsanity.com/paper/2302.01329/) (2023)
- [StableVideo: Text-driven Consistency-aware Diffusion Video Editing](https://mlsanity.com/paper/2308.09592/) (2023)
- [MotionDirector: Motion Customization of Text-to-Video Diffusion Models](https://mlsanity.com/paper/2310.08465/) (2023)
- [FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing](https://mlsanity.com/paper/2310.05922/) (2023)
- [Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models](https://mlsanity.com/paper/2303.17599/) (2023)
- [Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners](https://mlsanity.com/paper/2402.17723/) (2024)
- [FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://mlsanity.com/paper/2312.07537/) (2023)
- [VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2312.00845/) (2023)
- [Inst-Inpaint: Instructing to Remove Objects with Diffusion Models](https://mlsanity.com/paper/2304.03246/) (2023)
- [Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models](https://mlsanity.com/paper/2310.01107/) (2023)
- [UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing](https://mlsanity.com/paper/2402.13185/) (2024)

## Built on by (most cited first, 5 of 5)

- [Insert Anything: Image Insertion via In-Context Editing in DiT](https://mlsanity.com/paper/2504.15009/) (2025)
- [AVI-Edit: Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner](https://mlsanity.com/paper/2512.10571/) (2025)
- [A2-Edit: Precise Reference-Guided Image Editing of Arbitrary Objects and Ambiguous Masks](https://mlsanity.com/paper/2603.10685/) (2026)
- [Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction](https://mlsanity.com/paper/2510.22335/) (2025)
- [EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation](https://mlsanity.com/paper/2608.02474/) (2026)

## Abstract (arXiv)

Audio-driven visual scene editing endeavors to manipulate the visual background while leaving the foreground content unchanged, according to the given audio signals. Unlike current efforts focusing primarily on image editing, audio-driven video scene editing has not been extensively addressed. In this paper, we introduce AudioScenic, an audio-driven framework designed for video scene editing. AudioScenic integrates audio semantics into the visual scene through a temporal-aware audio semantic injection process. As our focus is on background editing, we further introduce a SceneMasker module, which maintains the integrity of the foreground content during the editing process. AudioScenic exploits the inherent properties of audio, namely, audio magnitude and frequency, to guide the editing process, aiming to control the temporal dynamics and enhance the temporal consistency. First, we present an audio Magnitude Modulator module that adjusts the temporal dynamics of the scene in response to changes in audio magnitude, enhancing the visual dynamics. Second, the audio Frequency Fuser module is designed to ensure temporal consistency by aligning the frequency of the audio with the dynamics of the video scenes, thus improving the overall temporal coherence of the edited videos. These integrated features enable AudioScenic to not only enhance visual diversity but also maintain temporal consistency throughout the video. We present a new metric named temporal score for more comprehensive validation of temporal consistency. We demonstrate substantial advancements of AudioScenic over competing methods on DAVIS and Audioset datasets.