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

Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation