  Creating a 360{\deg} parametric model of a human head is a very challenging
task. While recent advancements have demonstrated the efficacy of leveraging
synthetic data for building such parametric head models, their performance
remains inadequate in crucial areas such as expression-driven animation,
hairstyle editing, and text-based modifications. In this paper, we build a
dataset of artist-designed high-fidelity human heads and propose to create a
novel parametric 360{\deg} renderable parametric head model from it. Our scheme
decouples the facial motion/shape and facial appearance, which are represented
by a classic parametric 3D mesh model and an attached neural texture,
respectively. We further propose a training method for decompositing hairstyle
and facial appearance, allowing free-swapping of the hairstyle. A novel
inversion fitting method is presented based on single image input with high
generalization and fidelity. To the best of our knowledge, our model is the
first parametric 3D full-head that achieves 360{\deg} free-view synthesis,
image-based fitting, appearance editing, and animation within a single model.
Experiments show that facial motions and appearances are well disentangled in
the parametric space, leading to SOTA performance in rendering and animating
quality. The code and SynHead100 dataset are released at
https://nju-3dv.github.io/projects/Head360.
