arXiv:2601. 00664v2 Announce Type: replace-cross Abstract: Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation.
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Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing approaches struggle to maintain visual temporal consistency and fail to explicitly perceive user intent in complex interactive streaming scenarios.
The paper introduces an end‑to‑end framework for personalized audio‑driven facial motion that operates in real time without look‑ahead. It combines a causal multi‑resolution motion tokenizer, which captures both global temporal context and fine articulatory details, with a multi‑modal style retriever that pulls stylistic priors from arbitrary reference footage using ongoing audio and motion queries. This approach allows high‑fidelity, identity‑consistent animation from just a few casually recorded clips, outperforming existing methods in lip‑sync accuracy, identity consistency, and perceived realism while maintaining real‑time streaming constraints.
arXiv:2608. 10720v1 Announce Type: new Abstract: Omni-modal dialogue models can understand multimodal inputs and synthesize spoken replies, yet their responses remain visually disembodied.
arXiv:2609.39273v1 Announce Type: new Abstract: This report presents \textbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with pr...
arXiv:2609.13264v1 Announce Type: cross Abstract: Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In additi...
arXiv:2606. 30145v1 Announce Type: new Abstract: Natural face-to-face conversation requires real-time speech generation together with synchronized facial motion.