HeadGAP is an advanced 3D avatar creation model that can create realistic and animatable 3D avatars from a few or even a single image of the target individual. This model learns 3D head priors by leveraging a large-scale multi-view dynamic dataset and utilizes a Gaussian Splatting-based auto-decoding network for dynamic modeling. HeadGAP enables fast avatar personalization by learning the properties of Gaussian primitives through identity-sharing encoding and personalized latent codes.