Personalized Game Character Design via Fast Style Transfer API Integration

Abstract

As player expectations for personalized visual identities continue to rise, traditional game character design workflows struggle to efficiently support diverse artistic customization. Manual character styling is time-consuming, costly, and difficult to scale in real-time interactive environments. To address this challenge, this study proposes a fast neural style transfer (FST) framework for real-time game character customization. The proposed system integrates a feed-forward convolutional neural network with perceptual loss and Adaptive Instance Normalization (AdaIN) to enable efficient and flexible artistic style transfer for both 2D characters and 3D texture maps. The framework supports arbitrary and interpolated styles while preserving character structure and visual consistency. To evaluate system performance, we conduct quantitative experiments using SSIM, FID, and inference latency, alongside user-centered evaluations measuring usability and aesthetic satisfaction. Experimental results demonstrate that the proposed method achieves real-time performance with an average inference latency of 43.2 ms, while maintaining high visual fidelity and strong content preservation. User studies further indicate high usability and personalization satisfaction. Overall, this work demonstrates the feasibility of integrating fast neural style transfer into interactive game pipelines and provides practical insights for AI-assisted character customization systems.