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

从单张肖像创建游戏角色面孔的 MeInGame

MeInGame: Create a Game Character Face from a Single Portrait

Game character customization is one of the core features of many recent
Role-Playing Games (RPGs), where players can edit the appearance of their
in-game characters with their preferences. This paper studies the problem of
automatically creating in-game characters with a single photo. In recent
literature on this topic, neural networks are introduced to make game engine
differentiable and the self-supervised learning is used to predict facial
customization parameters. However, in previous methods, the expression
parameters and facial identity parameters are highly coupled with each other,
making it difficult to model the intrinsic facial features of the character.
Besides, the neural network based renderer used in previous methods is also
difficult to be extended to multi-view rendering cases. In this paper,
considering the above problems, we propose a novel method named "PokerFace-GAN"
for neutral face game character auto-creation. We first build a differentiable
character renderer which is more flexible than the previous methods in
multi-view rendering cases. We then take advantage of the adversarial training
to effectively disentangle the expression parameters from the identity
parameters and thus generate player-preferred neutral face (expression-less)
characters. Since all components of our method are differentiable, our method
can be easily trained under a multi-task self-supervised learning paradigm.
Experiment results show that our method can generate vivid neutral face game
characters that are highly similar to the input photos. The effectiveness of
our method is verified by comparison results and ablation studies.

本文提出了一种基于 PokerFace-GAN 的游戏角色自动创建方法，通过建立一个不同 iable 的角色渲染器来更加灵活地进行多视角渲染，并利用对抗性训练有效地将表情参数与身份参数相分离，从而生成玩家所喜爱的中立面（表情无表达的）角色。经过实验证明，本文的方法可以生成非常类似于输入照片的生动中立面游戏角色。