A State-of-the-Art Review of the Real-time Deformable Model Using Novel Approaches and Deep Learning

• Hongly Va,
• Nak-Jun Sung,
• Makara Mao,
• Jun-Ma,
• Min-Hyung Choi,
• Min Hong,

Vol. 19, No. 9, September 30, 2025
10.3837/tiis.2025.09.003, Download Paper (Free):

• 3D simulation
• Cloth simulation
• FEM
• Mass-spring model
• Neural Network
• PBD
• Physically-based simulation

Abstract

Real-time physically-based simulations (PBS) have become essential in various industries, including immersive content, medical imaging, architecture, and entertainment. While Conventional PBS techniques have been proposed over the years to improve the efficiency, visual realism, and speed of the animation, a detail review that critically evaluates their strengths and weaknesses for deformable objects is currently lacking. Therefore, in this paper, we filled this gap by presenting a comprehensive review of existing techniques for deformable object simulation and organize them based on simulation method, model representation, and recent applications, which provides a comparison of different applications. Our analysis highlights the strengths and limitations of existing physically-based simulation methods for deformable objects, including Finite Element Method (FEM), Mass-Spring Method (MSM), and Position-based Dynamic (PBD). Furthermore, we specifically present how deep learning techniques creatively address persistent issues related to stability and real-time performance in various application areas.

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