Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position of bones) and discrete parameters (like the hairstyles) makes it possible for users to personalize and customize in-game characters. Previous in-game character auto-creation systems are mostly image-driven, where facial parameters are optimized so that the rendered character looks similar to the reference face photo. This paper proposes a novel text-to-parameter translation method (T2P) to achieve zero-shot text-driven game character auto-creation. With our method, users can create a vivid in-game character with arbitrary text description without using any reference photo or editing hundreds of parameters manually. In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework. Due to the discontinuous parameter representation, previous methods have difficulty in effectively learning discrete facial parameters. T2P, to our best knowledge, is the first method that can handle the optimization of both discrete and continuous parameters. Experimental results show that T2P can generate high-quality and vivid game characters with given text prompts. T2P outperforms other SOTA text-to-3D generation methods on both objective evaluations and subjective evaluations.

翻译：近期流行的角色扮演游戏（RPGs）中，角色自动创建系统取得了巨大成功。由连续参数（如骨骼位置）和离散参数（如发型）驱动的骨骼面部模型，使得用户能够个性化定制游戏内角色。以往游戏内的角色自动创建系统多数基于图像驱动，通过优化面部参数使渲染的角色与参考面部照片相似。本文提出一种新颖的文本到参数翻译方法（T2P），实现零样本文本驱动的游戏角色自动创建。利用该方法，用户无需参考照片或手动编辑数百个参数，仅凭任意文本描述即可创建生动的游戏角色。在我们的方法中，借助大规模预训练多模态CLIP与神经渲染的能力，T2P在统一框架内同时搜索连续面部参数和离散面部参数。由于参数表示的不连续性，以往方法难以有效学习离散面部参数。据我们所知，T2P是首个能够处理离散与连续参数优化的方法。实验结果表明，T2P能根据给定文本提示生成高质量、生动的游戏角色。在客观评估与主观评估中，T2P均优于其他最先进的文本到3D生成方法。