AI killed the video star. Audio-driven diffusion model for expressive talking head generation
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
We propose Dimitra++, a novel framework for audio-driven talking head generation, streamlined to learn lip motion, facial expression, as well as head pose motion. Specifically, we propose a conditional Motion Diffusion Transformer (cMDT) to model facial motion sequences, employing a 3D representation. The cMDT is conditioned on two inputs: a reference facial image, which determines appearance, as well as an audio sequence, which drives the motion. Quantitative and qualitative experiments, as well as a user study on two widely employed datasets, i.e., VoxCeleb2 and CelebV-HQ, suggest that Dimitra++ is able to outperform existing approaches in generating realistic talking heads imparting lip motion, facial expression, and head pose.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 64 of its 77 references that are in the map. First posted on arXiv 27 Nov 2025.
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64 of 77 references.
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- 2023Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation6 Jan
- 2022Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors7 Dec
Other references · 59
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- 2018VoxCeleb2: Deep Speaker Recognitioncited by 875
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38 more
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- 2025StreamingTalker: Audio-driven 3D Facial Animation with Autoregressive Diffusion Model
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