Instant Expressive Gaussian Head Avatars at Over 100 FPS

Portrait animation has witnessed tremendous quality im-provements thanks to recent advances in video diffusion models. How-ever, these 2D methods often compromise 3D consistency and speed, lim-iting their applicability in real-world scenarios, such as digital twins ortelepresence. In contrast, 3D-aware feedforward facial animation methods– built upon 3D representations, such as neural radiance fields or Gaus-sian splatting – ensure 3D consistency and achieve faster inference speed,but come with inferior expression details. In this paper, we address thisportrait animation trilemma (speed, 3D consistency, and expressiveness)and propose a pipeline that instantly converts an in-the-wild single im-age into a 3D-consistent, fast yet expressive animatable representationvia a feed-forward encoder. Unlike previous computationally intensiveglobal fusion mechanisms (e.g., multiple attention layers) for fusing 3Dstructural and animation information, our design employs an efficientlightweight local fusion strategy to achieve high animation expressivity.Furthermore, our animation representation is decoupled from the face’s3D representation and learns motion implicitly from data, eliminatingthe dependency on pre-defined parametric models that often constrainanimation capabilities. Our method runs at 107.31 FPS for animationand pose control, representing a 3-4 order of magnitude speedup versusthe state of the art while achieving comparable animation quality, thussurpassing alternative designs that trade speed for quality or vice versa.