arXiv:2606. 30145v1 Announce Type: new Abstract: Natural face-to-face conversation requires real-time speech generation together with synchronized facial motion.
By Habin Lim, Jae-Ho Lee, Hah Min Lew, Ji-Su Kang, Gyeong-Moon Park
arXiv:2609.22913v1 Announce Type: cross
Abstract: Talking-head and dyadic models now achieve real-time inference, yet fast motion generation alone does not produce an interactive conversation. A live...
By Artem Kravtsov, Dmitrii Ziganshin, Vsevolod Poletaev, Gleb Balitskiy, Anastasia Tikhonova, Egor Burkov, Vadim Lebedev
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.
By Xuangeng Chu, Yu Han, Wei Mao, Shih-En Wei
arXiv:2606. 01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temporal correspondence between generated and reference videos.
By Zhicheng Zhang, Lei Wang, Yu Zhang, Yongsheng Gao
GestureFAR is a flow‑autoregressive framework that generates natural co‑speech gestures from streaming speech while preserving causality and continuous motion expressiveness. It autoregresses over continuous motion latents using a transformer for audio‑motion context and a flow‑matching head to sample the next latent. A head‑only flow distillation strategy further reduces latency by collapsing multi‑step flow sampling into a single network evaluation, enabling real‑time token‑causal generation with improved quality‑latency trade‑off on the BEAT2 benchmark.
By Pinxin Liu, Haiyang Liu, Jiahao Luo, Junhua Huang, Chunhao Zou, Luchuan Song
arXiv:2606. 28568v1 Announce Type: cross Abstract: Speech-driven 3D facial animation methods face significant challenges in simultaneously achieving high-fidelity motion and precise artistic control at production quality.
By Arthur Josi, Emeline Got, Abdallah Dib, Luiz Gustavo Hafemann, Rafael M. O. Cruz