  In the context of Industry 4.0, digital twin technology has emerged with
rapid advancements as a powerful tool for visualizing and analyzing industrial
assets. This technology has attracted considerable interest from researchers
across diverse domains such as manufacturing, security, transportation, and
gaming. The metaverse has emerged as a significant enabler in these domains,
facilitating the integration of various technologies to create virtual replicas
of physical assets. The utilization of 3D character animation, often referred
to as avatars, is crucial for implementing the metaverse. Traditionally, costly
motion capture technologies are employed for creating a realistic avatar
system. To meet the needs of this evolving landscape, we have developed a
modular framework tailored for asset digital twins as a more affordable
alternative. This framework offers flexibility for the independent
customization of individual system components. To validate our approach, we
employ the English peg solitaire game as a use case, generating a solution tree
using the breadth-first search algorithm. The results encompass both
qualitative and quantitative findings of a data-driven 3D animation system
utilizing motion primitives. The presented methodologies and infrastructure are
adaptable and modular, making them applicable to asset digital twins across
diverse business contexts. This case study lays the groundwork for pilot
applications and can be tailored for education, health, or Industry 4.0
material development.
