面向游戏角色自动生成的面-参数转换
Face-to-Parameter Translation for Game Character Auto-Creation
摘要：
角色定制系统是角色扮演游戏（RPG）中的一个重要组成部分，允许玩家根据自己的喜好而不是使用默认模板来编辑游戏中角色的面部外观。提出了一种根据输入的人脸照片自动生成玩家游戏角色的方法。我们制定了面部相似性测量和参数搜索范式下的上述“艺术创作”的过程中，通过解决一个优化问题，在一个大的物理意义的面部参数。为了有效地最小化创建的人脸与真实人脸之间的距离，专门设计了两个损失函数，即“区分损失”和“人脸内容损失”。由于游戏引擎的渲染过程是不可微的，进一步引入生成网络作为“模仿者”来模仿游戏引擎的物理行为，使得所提出的方法可以在神经风格转换框架下实现，并且可以通过梯度下降来优化参数。实验结果表明，我们的方法实现了输入的人脸照片和创建的游戏中的字符在全局外观和局部细节方面的高度生成相似性。我们的方法已经在去年的一个新游戏中部署，现在已经被玩家使用了超过100万次。
英文摘要：
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates. This paper proposes a method for automatically creating in-game characters of players according to an input face photo. We formulate the above "artistic creation" process under a facial similarity measurement and parameter searching paradigm by solving an optimization problem over a large set of physically meaningful facial parameters. To effectively minimize the distance between the created face and the real one, two loss functions, i.e. a "discriminative loss" and a "facial content loss", are specifically designed. As the rendering process of a game engine is not differentiable, a generative network is further introduced as an "imitator" to imitate the physical behavior of the game engine so that the proposed method can be implemented under a neural style transfer framework and the parameters can be optimized by gradient descent. Experimental results demonstrate that our method achieves a high degree of generation similarity between the input face photo and the created in-game character in terms of both global appearance and local details. Our method has been deployed in a new game last year and has now been used by players over 1 million times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
