  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.
