Comprehensive Review of Digital Twin Technology for Deformable Objects: Integration of Modeling, Rendering, and Simulation

Digital Twin (DT) technology replicates the real world within a virtual environment, enabling real-time mapping of dynamic states from the real world to its virtual replica. This paper comprehensively reviews recent research methods in modeling, rendering, and simulation of deformable objects within DT technology. While existing DT technologies primarily focus on static or rigid objects, deformable objects exhibit continuous and nonlinear deformations due to external forces and interactions, presenting challenges such as real-time tracking, representation of complex material behavior, and simulation stability. To address these challenges, this review systematically analyzes modeling techniques categorized by input data type (multi-view images, point clouds, voxels, and single-view images), rendering methods emphasizing Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), and various simulation approaches including Finite Element Method (FEM), Graph neural network (GNN), and differentiable physics-based simulations from recent studies within the last five years. Additionally, the paper investigates integrated cases of these modeling, rendering, and simulation techniques, evaluating their compatibility and discussing their technical limitations. The analysis concludes that 3DGS has recently gained prominence over NeRF due to its faster processing speed and superior rendering quality. Moreover, advancements in machine learning and differentiable physics-based simulations are anticipated to be pivotal in expanding the applicability of DT technologies for deformable objects.