FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait
On the map: Diffusion models → Audio, speech and music
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.
Key facts. Cited by 40 papers in the map (data through 7 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.
Builds on
84 of 105 references.
Closest ancestors · 4
Same direction, newest first
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- 2023DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models30 Sep
- 2023Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation6 Jan
Other references · 80
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