# Speech Driven Video Editing via an Audio-Conditioned Diffusion Model

arXiv: https://arxiv.org/abs/2301.04474 · HTML: https://mlsanity.com/paper/2301.04474/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 80 of its 106 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 30 of 80)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [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)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)

## Built on by (most cited first, 28 of 28)

- [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023)
- [FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using Diffusion](https://mlsanity.com/paper/2309.11306/) (2023)
- [AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding](https://mlsanity.com/paper/2405.03121/) (2024)
- [LatentSync: Taming Audio-Conditioned Latent Diffusion Models for Lip Sync with SyncNet Supervision](https://mlsanity.com/paper/2412.09262/) (2024)
- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.09767/) (2023)
- [Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation](https://mlsanity.com/paper/2604.23632/) (2026)
- [KeySync: A Robust Approach for Leakage-free Lip Synchronization in High Resolution](https://mlsanity.com/paper/2505.00497/) (2025)
- [DiffDub: Person-generic Visual Dubbing Using Inpainting Renderer with Diffusion Auto-encoder](https://mlsanity.com/paper/2311.01811/) (2023)
- [Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator](https://mlsanity.com/paper/2305.02594/) (2023)
- [DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework for Talking Head Video Generation](https://mlsanity.com/paper/2410.13726/) (2024)
- [Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space Guided Diffusion](https://mlsanity.com/paper/2502.07203/) (2025)
- [HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation](https://mlsanity.com/paper/2310.05720/) (2023)
- [Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions](https://mlsanity.com/paper/2507.02900/) (2025)
- [A Comprehensive Taxonomy and Analysis of Talking Head Synthesis: Techniques for Portrait Generation, Driving Mechanisms, and Editing](https://mlsanity.com/paper/2406.10553/) (2024)
- [DiT-Head: High-Resolution Talking Head Synthesis using Diffusion Transformers](https://mlsanity.com/paper/2312.06400/) (2023)
- [Speech2rtMRI: Speech-Guided Diffusion Model for Real-time MRI Video of the Vocal Tract during Speech](https://mlsanity.com/paper/2409.15525/) (2024)
- [Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization](https://mlsanity.com/paper/2410.14283/) (2024)
- [Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism](https://mlsanity.com/paper/2312.06613/) (2023)
- [Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation](https://mlsanity.com/paper/2408.01732/) (2024)
- [High-fidelity and Lip-synced Talking Face Synthesis via Landmark-based Diffusion Model](https://mlsanity.com/paper/2408.05416/) (2024)
- [Removing Averaging: Personalized Lip-Sync Driven Characters Based on Identity Adapter](https://mlsanity.com/paper/2503.06397/) (2025)
- [RASA: Replace Anyone, Say Anything -- A Training-Free Framework for Audio-Driven and Universal Portrait Video Editing](https://mlsanity.com/paper/2503.11571/) (2025)
- [AUHead: Realistic Emotional Talking Head Generation via Action Units Control](https://mlsanity.com/paper/2602.09534/) (2026)
- [Geometry-guided Emotion Modulation for Controllable and Photorealistic Emotional Talking Face Generation](https://mlsanity.com/paper/2608.00663/) (2026)
- [Embedded Representation Learning Network for Animating Styled Video Portrait](https://mlsanity.com/paper/2404.19038/) (2024)
- [LatentColorization: Latent Diffusion-Based Speaker Video Colorization](https://mlsanity.com/paper/2405.05707/) (2024)
- [FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs](https://mlsanity.com/paper/2512.20033/) (2025)

## Abstract (arXiv)

Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronized without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model on audio mel spectral features to generate synchronised facial motion. Proof of concept results are demonstrated on both single-speaker and multi-speaker video editing, providing a baseline model on the CREMA-D audiovisual data set. To the best of our knowledge, this is the first work to demonstrate and validate the feasibility of applying end-to-end denoising diffusion models to the task of audio-driven video editing.