  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.
