With the rapid development of Role-Playing Games (RPGs), players are now
allowed to edit the facial appearance of their in-game characters with their
preferences rather than using default templates. This paper proposes a game
character auto-creation framework that generates in-game characters according
to a player's input face photo. Different from the previous methods that are
designed based on neural style transfer or monocular 3D face reconstruction, we
re-formulate the character auto-creation process in a different point of view:
by predicting a large set of physically meaningful facial parameters under a
self-supervised learning paradigm. Instead of updating facial parameters
iteratively at the input end of the renderer as suggested by previous methods,
which are time-consuming, we introduce a facial parameter translator so that
the creation can be done efficiently through a single forward propagation from
the face embeddings to parameters, with a considerable 1000x computational
speedup. Despite its high efficiency, the interactivity is preserved in our
method where users are allowed to optionally fine-tune the facial parameters on
our creation according to their needs. Our approach also shows better
robustness than previous methods, especially for those photos with head-pose
variance. Comparison results and ablation analysis on seven public face
verification datasets suggest the effectiveness of our method.

本研究提出一种游戏角色自动生成框架，根据玩家的输入面部照片生成游戏角色。通过自我监督学习范式下预测一大组具有物理意义的面部参数，提高了效率，并保留了互动性，在七个公共面部验证数据集上显示出更好的鲁棒性，特别是对于具有头部姿势变化的照片。