In this study, we present a method for creating a synthetic dataset using the MetaHuman framework to optimize the skinning of 3D models. The research focuses on improving the quality of skeletal deformation (skinning) by leveraging a diverse array of high-fidelity virtual human models. Using MetaHuman, we generated an extensive dataset comprising dozens of virtual characters with varied anthropometric fea-tures and precisely defined skinning weight parameters. This data was used to train an algorithm that optimizes the distribution of skinning weights between bones and the character mesh.

The proposed approach automates the weight rigging process, significantly reducing manual effort for riggers and increasing the accuracy of deformations during animation. Experimental results show that leveraging synthetic data reduces skinning errors and produces smoother character movements compared to traditional methods. The outcomes have direct applications in the video game, animation, virtual reality, and simulation industries, where rapid and high-quality rigging of numerous characters is required. The method can be integrated into existing graphics engines and development pipelines (such as Unreal Engine or Unity) as a plugin or tool, facilitating the adoption of this technology in practical projects.