arXiv Computer Vision By Yu Han, Dejan Markovic, Alexander Richard, Wojciech Zielonka, Akshay Venkatesh, Cheng-hsin Wuu, Michael Zollhoefer

No Distillation Needed: Single-Pass Real-Time Talking Heads via Acausal Noise Shaping

Read the original on arXiv Computer Vision →

The paper introduces FaceGAN, a single-pass GAN that generates real-time talking head animations from audio without the need for distillation or diffusion models. By shaping the noise pathway acausally, FaceGAN maintains a causal audio-to-expression mapping while suppressing unwanted spectral artifacts, enabling continuous, drift-free generation at interactive rates. The approach achieves comparable or superior quality to state‑of‑the‑art methods while requiring only one forward pass per frame.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Machine Learning
Sep 22

AVTR-1: Open Stack for Real-Time Interactive Avatars

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