FastAvatar: Instant 3D Gaussian Splatting for Faces from Single Unconstrained Poses

Hao Liang, Zhixuan Ge, Ashish Tiwari, Soumendu Majee, G.M. Dilshan Godaliyadda, Ashok Veeraraghavan, Guha Balakrishnan

Abstract:

We present FastAvatar, a fast and robust algorithm for
single-image 3D face reconstruction using 3D Gaussian
Splatting (3DGS). Given a single input image from an
arbitrary pose, FastAvatar recovers a high-quality, fullhead 3DGS avatar in approximately 3 seconds on a single NVIDIA A100 GPU. We use a two-stage design: a
feed-forward encoder–decoder predicts coarse face geometry by regressing Gaussian structure from a pose-invariant
identity embedding, and a lightweight test-time refinement
stage then optimizes the appearance parameters for photorealistic rendering. This hybrid strategy combines the
speed and stability of direct prediction with the accuracy
of optimization, enabling strong identity preservation even
under extreme input poses. FastAvatar achieves state-ofthe-art reconstruction quality (24.01 dB PSNR, 0.91 SSIM)
while running over 600× faster than existing per-subject
optimization methods (e.g., FlashAvatar, GaussianAvatars,
GASP). Once reconstructed, our avatars support photorealistic novel-view synthesis and FLAME-guided expression
animation, enabling controllable reenactment from a single
image. By jointly offering high fidelity, robustness to pose,
and rapid reconstruction, FastAvatar significantly broadens
the applicability of 3DGS-based facial avatars.