Objective

2

Cloth simulation has a long history of research and is important in the fields of physical simulation

computer games

virtual reality

etc. With the continuous development of computer software and hardware technology

virtual simulation has become a research hotspot in the field of computer graphics. As a classic flexible object

cloth can be seen everywhere in people's lives. Effectively and realistically simulating the movement of fabrics (such as flags

curtains

tablecloths

etc.) on a computer remains challenging and needs to be solved. Realistic virtual human costume animation can bring a strong visual reality to virtual characters and has broad application prospects in cultural creative fields

such as animation

game entertainment

film and television. In addition

the method can be applied to the apparel industry for virtual clothing design and display. In recent years

with the emergence of applications

such as virtual reality and human-computer interaction

especially the rise of network virtual environments with high user-interaction characteristics

the demand for real-time virtual human costume animations

such as online games and distributed collaborative design environments

has increased. Neural network models are used to train the data and improve the simulation rate of fabrics for multi-precision cloth division threshold according to the degree of deformation of different regions

rather than manual threshold division

during the cloth simulation. However

the traditional physics-based method is still used to calculate the particle position in cloth simulation. Physics-based simulation methods are computationally complex

time consuming

and perform poorly in real time. Although considerable research has been conducted to improve physics-based methods

such as the recent alternating direction method of multipliers (ADMM) algorithm

further improvements can be made. This paper proposes a layered simulation of cloth based on the random forest model

instead of the physics-based simulation method.

Method

2

We present fabric layered modeling method based on random forest algorithm

and its working principle is as follows. The initial levels of fabric are simulated using traditional physics-based methods. Implicit integration method is used to calculate the initial position of each particle in the initial horizontal fabric. The index value of the triangle generated by the above steps is recorded as 0. High levels of fabric are then simulated using the random forest model. The random forest algorithm is applied to predict the position of each particle in the initial horizontal fabric in a higher level of fabric. One is added to the index value of the newly generated triangle in the above step. This article uses the

$$\sqrt 3 $$

subdivision method to divide the mesh fabric; as such

edge flip operation should be performed to eliminate the narrow triangle. When the triangle generated in the above step has a triangle that shares the edge with the edge corresponding to the obtuse angle in the triangle and the two triangles have the same index value

the edge flip operation is performed. One is added to the index value of the new triangle generated by the edge flip operation. Whether the cloth animation generated at this time can meet the demand is determined. If the requirements are met

then the simulation is terminated. If the requirements are not met

then steps 2 to 4 are repeated until satisfactory animation is produced.

Result

2

The capacity of the regression tree in the random forest model is the lowest when the simulation takes the lowest time. The number of iterations of fixed conjugate gradient method and alternating direction method of multiplier algorithm ensures that the simulation results by the two traditional methods have similar deviation. When the number of simulated particles is small

the random forest-based layered cloth simulation method has no significant advantage compared with the two traditional physics-based simulation methods. However

when simulating a sufficient number of particles

the simulation speed of the layered cloth simulation method based on the random forest model is about 26% higher than that of the ADMM algorithm. To verify whether the method can produce stable and reliable simulation results

we add wind force in the experiment. Random sampling is carried out to ensure that the experimental results are representative. The method can still produce stable and reliable cloth animation effects under the influence of external force.

Conclusion

2

This paper proposes a fabric layered modeling method based on the random forest algorithm. The fabric is hierarchically simulated by replacing the traditional physics-based integration method with a random forest algorithm. The random forest algorithm is used to predict the position of the particle in the next horizontal cloth

and the edge flip operation is used to generate a stable and reliable triangular mesh cloth. The feasibility and effectiveness of the stratified fabric simulation method based on random forest are verified from multiple evaluation indicators relative to the two traditional physics-based fabric simulation methods.