Many deep learning based 3D face reconstruction methods have been proposed
recently, however, few of them have applications in games. Current game
character customization systems either require players to manually adjust
considerable face attributes to obtain the desired face, or have limited
freedom of facial shape and texture. In this paper, we propose an automatic
character face creation method that predicts both facial shape and texture from
a single portrait, and it can be integrated into most existing 3D games.
Although 3D Morphable Face Model (3DMM) based methods can restore accurate 3D
faces from single images, the topology of 3DMM mesh is different from the
meshes used in most games. To acquire fidelity texture, existing methods
require a large amount of face texture data for training, while building such
datasets is time-consuming and laborious. Besides, such a dataset collected
under laboratory conditions may not generalized well to in-the-wild situations.
To tackle these problems, we propose 1) a low-cost facial texture acquisition
method, 2) a shape transfer algorithm that can transform the shape of a 3DMM
mesh to games, and 3) a new pipeline for training 3D game face reconstruction
networks. The proposed method not only can produce detailed and vivid game
characters similar to the input portrait, but can also eliminate the influence
of lighting and occlusions. Experiments show that our method outperforms
state-of-the-art methods used in games.

本研究提出一种自动化游戏角色面部创建方法，可以预测面部形状和纹理，并可以整合到大多数现有的三维游戏中，超越目前游戏中使用的最先进的方法