EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting

Character customization, or ``face crafting,'' is a vital feature in role-playing games (RPGs) that enhances player engagement by allowing the creation of personalized avatars. Existing automated methods often face challenges in maintaining style consistency and adaptability across different game engines due to their reliance on specific image domains. We introduce EasyCraft, an innovative end-to-end feedforward framework that automates character crafting by integrating both text and image inputs. Our approach utilizes a translator capable of converting facial images of any style into crafting parameters. To develop this translator, we first establish a unified feature distribution in the translator's image encoder through self-supervised learning on a large-scale dataset. Subsequently, we learn the mapping from the feature distribution to the crafting parameters specific to a game engine. By integrating text-to-image techniques with our translator, EasyCraft also supports precise, text-based character crafting. EasyCraft's integration of diverse inputs significantly enhances the versatility and accuracy of avatar creation. Extensive experiments on two RPG games demonstrate the effectiveness of our method, achieving state-of-the-art results and facilitating adaptability across various avatar engines.