GHARP: Real-time Gaussian Head Animation from a Large-Scale Reconstruction Prior
GHARP is a two-stage method that builds an offline identity representation of a subject (geometry and appearance) and a lightweight runtime animation network that predicts expression-dependent residuals in a semantically structured latent space of a pretrained reconstruction model. The paper introduces a body alignment network to resolve under-specified body/clothing regions, reports state-of-the-art results on the Ava-256 benchmark, and claims up to 13× faster runtime on an A100 GPU with 8× fewer Gaussians.