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.

本研究解决了创建360°参数化人头模型中面部表情动画、发型编辑及基于文本的修改等关键领域表现不足的问题。提出了一种新方法，通过艺术家设计的高保真头部数据集，开发出一种参数化可渲染的3D头部模型，能够在单一模型中实现360°自由视角合成、图像拟合、外观编辑和动画。这一模型在渲染和动画质量上表现出色，具有显著的潜在影响。

Head360：学习用于360°自由视角合成的参数化3D全头模型