While the cloth component in Unity engine has been used to represent the 3D cloth object for augmented reality (AR) and virtual reality (VR), it has several limitations in term of resolution and performance. The purpose of our research is to develop a stable cloth simulation based on a parallel algorithm. The method of a mass–spring system is applied to real-time cloth simulation with three types of springs. However, cloth simulation using the mass–spring system requires a small integration time-step to use a large stiffness coefficient. Furthermore, constraint enforcement is applied to obtain the stable behavior of the cloth model. To reduce the computational burden of constraint enforcement, the adaptive constraint activation and deactivation (ACAD) technique that includes the mass–spring system and constraint enforcement method is applied to prevent excessive elongation of the cloth. The proposed algorithm utilizes the graphics processing unit (GPU) parallel processing, and implements it in Compute Shader that executes in different pipelines to the rendering pipeline. In this paper, we investigate the performance and compare the behavior of the mass–spring system, constraint enforcement, and ACAD techniques using a GPU-based parallel method.

This paper proposes an efficient approach for simulating volumetric deformable objects using the Position-Based Dynamics (PBD) method. Volumetric bodies generated by TetGen are used to represent three-dimensional objects, which accurately capture complex shapes and volumes. However, when a large number of constraints are applied to the system to solve using serialized algorithms on central processing units (CPU), the computational cost can become a bottleneck of the simulation. To address this issue, the proposed implementation algorithm takes advantage of graphic processing unit (GPU) acceleration and parallel processing to improve the efficiency of the simulation. We propose two specific contributions: firstly, the use of the PBD method with volume constraint for tetrahedral elements to simulate volumetric deformable objects realistically; secondly, an efficient GPU-accelerated algorithm for implementing the PBD method that significantly improves computational efficiency. We also applied the node-centric and constraint-centric algorithms to solve the stretch constraint in the GPU-based algorithm. The implementation was performed using Unity3D. The compute shader feature of Unity3D was utilized to perform thousands of parallel computations in a single pass, making it possible to simulate large and complex objects in real-time. The performance of the simulation can be accelerated by using GPU-based methods with stretch and bending constraints, which provides significant speedup factors compared to using only the CPU for deformable objects such as Bunny, Armadillo, and Dragon. The constraint-centric and node-centric GPU approaches provide speedup factors of up to 8.9x and 8x, respectively, while the GPU-based methods with all types of constraints exhibit a slight decrease but still operate at real-time speeds. Overall, this approach enables the simulation of complex and irregular shapes with plausible and realistic results, while also achieving speed, robustness, and flexibility. Additionally, the proposed approach can be applied to general simulation and other game engines that support GPU-based acceleration.

In virtual reality, augmented reality, or animation, the goal is to represent the movement of deformable objects in the real world as similar as possible in the virtual world. Therefore, this paper proposed a method to automatically extract cloth stiffness values from video scenes, and then they are applied as material properties for virtual cloth simulation. We propose the use of deep learning (DL) models to tackle this issue. The Transformer model, in combination with pre-trained architectures like DenseNet121, ResNet50, VGG16, and VGG19, stands as a leading choice for video classification tasks. Position-Based Dynamics (PBD) is a computational framework widely used in computer graphics and physics-based simulations for deformable entities, notably cloth. It provides an inherently stable and efficient way to replicate complex dynamic behaviors, such as folding, stretching, and collision interactions. Our proposed model characterizes virtual cloth based on softness-to-stiffness labels and accurately categorizes videos using this labeling. The cloth movement dataset utilized in this research is derived from a meticulously designed stiffness-oriented cloth simulation. Our experimental assessment encompasses an extensive dataset of 3840 videos, contributing to a multi-label video classification dataset. Our results demonstrate that our proposed model achieves an impressive average accuracy of 99.50%. These accuracies significantly outperform alternative models such as RNN, GRU, LSTM, and Transformer.

Physically based cloth simulation requires a model that represents cloth as a collection of nodes connected by different types of constraints. In this paper, we present a coefficient prediction framework using a Deep Learning (DL) technique to enhance video summarization for such simulations. Our proposed model represents virtual cloth as interconnected nodes that are subject to various constraints. To ensure temporal consistency, we train the video coefficient prediction using Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM), and Transformer models. Our lightweight video coefficient network combines Convolutional Neural Networks (CNN) and a Transformer to capture both local and global contexts, thus enabling highly efficient prediction of keyframe importance scores for short-length videos. We evaluated our proposed model and found that it achieved an average accuracy of 99.01%. Specifically, the accuracy for the coefficient prediction of GRU was 20%, while LSTM achieved an accuracy of 59%. Our methodology leverages various cloth simulations that utilize a mass-spring model to generate datasets representing cloth movement, thus allowing for the accurate prediction of the coefficients for virtual cloth within physically based simulations. By taking specific material parameters as input, our model successfully outputs a comprehensive set of geometric and physical properties for each cloth instance. This innovative approach seamlessly integrates DL techniques with physically based simulations, and it therefore has a high potential for use in modeling complex systems.

This paper describes a parallel method to simulate real-time 3D deformable objects using volume preservation constraints on a mass-spring model (MSM) to achieve plausible results in real-time performance. Instead of considering a volumetric mesh which is mostly used to simulate deformable objects, we purely take the surface of the 3D object into account to reduce the time complexity and obtain a high-quality deformable. In the conventional MSM, we can simply control the shape of the deformable object through the stiffness and damping coefficients which is beneficial for our volume constraint. We use the divergence theorem and implicit constraint enforcement scheme to maintain the volume of the object and deform it freely. The surface-based volume constraint is applied to correct the force of the spring network. The proposed algorithm was designed on compute shader in Unity3D and runs outside the normal rendering pipeline in the graphics processing unit (GPU) in order to utilize the massive parallel process to accelerate the performance of the simulation. The performance of the simulation can be accelerated by using the parallel processing method on the GPU with an average speedup factor of 4.27 using the conventional mass-spring method, and an average speedup factor of 2.54 for the volume preservation constraint method. We present several scenes which demonstrate the volume-preserving deformations using the conventional mass-spring method and volume-preservation constraint method and the volume loss of deformable objects for all 3D models is compared. The volume preservation method obtains a volume loss significantly lower than the conventional MSM for all experimental tests.

Real-time physics simulations are vital for creating immersive and interactive experiences in extended reality (XR) applications. Balancing computational efficiency and simulation accuracy is challenging, especially in environments with multiple deformable objects that require complex interactions. In this study, we introduce a GPU-based parallel processing framework combined with a position-based dynamics (PBD) solver to tackle these challenges. The system is deployed within the Unity engine and enhances real-time performance through the use of sophisticated collision detection and response algorithms. Our method employs an AABB-based bounding volume hierarchy (BVH) structure to efficiently detect collisions, and incorporates the Möller–Trumbore algorithm for precise triangle-level interactions. We also boost computational efficiency by storing collision data in GPU-accessible 2D textures. Experimental assessments show performance improvements of up to 1705% in GPU simulations over CPU counterparts, achieving stable real-time frame rates for complex models such as the Stanford Bunny and Armadillo. Furthermore, utilizing 2D texture storage improves the FPS by up to 117%, confirming its efficacy for XR applications. This study offers a robust, scalable framework for real-time physics simulations, facilitating more natural and immersive XR experiences.