# FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait

arXiv: https://arxiv.org/abs/2412.01064 · HTML: https://mlsanity.com/paper/2412.01064/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 84 of its 105 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Placed in

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

## Builds on (most cited first, 30 of 84)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [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)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)

## Built on by (most cited first, 30 of 40)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [Wan-Animate: Unified Character Animation and Replacement with Holistic Replication](https://mlsanity.com/paper/2509.14055/) (2025)
- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation](https://mlsanity.com/paper/2601.00664/) (2026)
- [Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations](https://mlsanity.com/paper/2510.05571/) (2025)
- [FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation](https://mlsanity.com/paper/2511.21029/) (2025)
- [OmniTalker: One-shot Real-time Text-Driven Talking Audio-Video Generation With Multimodal Style Mimicking](https://mlsanity.com/paper/2504.02433/) (2025)
- [DeX-Portrait: Disentangled and Expressive Portrait Animation via Explicit and Latent Motion Representations](https://mlsanity.com/paper/2512.15524/) (2025)
- [THEval. Evaluation Framework for Talking Head Video Generation](https://mlsanity.com/paper/2511.04520/) (2025)
- [Learning Robot Manipulation from Audio World Models](https://mlsanity.com/paper/2512.08405/) (2025)
- [Controllable and Expressive One-Shot Video Head Swapping](https://mlsanity.com/paper/2506.16852/) (2025)
- [A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World](https://mlsanity.com/paper/2512.04837/) (2025)
- [Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling](https://mlsanity.com/paper/2604.23586/) (2026)
- [CogPortrait: Fine-Grained Eye-Region Control in Portrait Animation via Hierarchical Agent Planning](https://mlsanity.com/paper/2605.28056/) (2026)
- [Few-Shot Identity Adaptation for 3D Talking Heads via Global Gaussian Field](https://mlsanity.com/paper/2506.22044/) (2025)
- [IMTalker: Efficient Audio-driven Talking Face Generation with Implicit Motion Transfer](https://mlsanity.com/paper/2511.22167/) (2025)
- [IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation](https://mlsanity.com/paper/2602.07498/) (2026)
- [Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video](https://mlsanity.com/paper/2604.07786/) (2026)
- [PortraitDirector: A Hierarchical Disentanglement Framework for Controllable and Real-time Facial Reenactment](https://mlsanity.com/paper/2604.19129/) (2026)
- [DEMO: Disentangled Motion Latent Flow Matching for Fine-Grained Controllable Talking Portrait Synthesis](https://mlsanity.com/paper/2510.10650/) (2025)
- [STARCaster: Spatio-Temporal AutoRegressive Video Diffusion for Identity- and View-Aware Talking Portraits](https://mlsanity.com/paper/2512.13247/) (2025)
- [FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs](https://mlsanity.com/paper/2512.20033/) (2025)
- [Rethinking Refinement: Correcting Generative Bias without Noise Injection](https://mlsanity.com/paper/2601.21182/) (2026)
- [Asymmetric Hierarchical Anchoring for Robust Audio-Visual Cross-Modal Generalization](https://mlsanity.com/paper/2602.03570/) (2026)
- [Native Extrapolation Awareness in Flow-Based Conditional Generation](https://mlsanity.com/paper/2602.13061/) (2026)
- [EmbedTalk: Talking Head Synthesis using Gaussian Embeddings](https://mlsanity.com/paper/2603.07604/) (2026)
- [ECHO: Towards Emotionally Appropriate and Contextually Aware Interactive Head Generation](https://mlsanity.com/paper/2603.17427/) (2026)
- [GazeCLIP: Gaze-Guided CLIP with Adaptive-Enhanced Fine-Grained Language Prompt for Deepfake Attribution and Detection](https://mlsanity.com/paper/2603.29295/) (2026)
- [MoCoTalk: Multi-Conditional Diffusion with Adaptive Router for Controllable Talking Head Generation](https://mlsanity.com/paper/2605.08050/) (2026)
- [Test-Time Self-Adaptive Conditioning for Stable Audio-Driven Talking-Head Generation](https://mlsanity.com/paper/2605.25488/) (2026)

## Abstract (arXiv)

With the rapid advancement of diffusion-based generative models, portrait image animation has achieved remarkable results. However, it still faces challenges in temporally consistent video generation and fast sampling due to its iterative sampling nature. This paper presents FLOAT, an audio-driven talking portrait video generation method based on flow matching generative model. Instead of a pixel-based latent space, we take advantage of a learned orthogonal motion latent space, enabling efficient generation and editing of temporally consistent motion. To achieve this, we introduce a transformer-based vector field predictor with an effective frame-wise conditioning mechanism. Additionally, our method supports speech-driven emotion enhancement, enabling a natural incorporation of expressive motions. Extensive experiments demonstrate that our method outperforms state-of-the-art audio-driven talking portrait methods in terms of visual quality, motion fidelity, and efficiency.