Multi‐mother wavelet neural network‐based on genetic algorithm and multiresolution analysis for fast 3D mesh deformation

Abstract

The current study presents a new 3D mesh deformation process using multi‐mother wavelet neural network architecture, which relies on genetic algorithm and multiresolution analysis. Classic forming algorithms begin with a predetermined network architecture that may be insufficient or too complicated. In addition, the solving of wavelet neural network training problems is described by their perceived inability to escape local optima. The main objective of the authors' proposed approach is that it prevents both the insufficiency and local minima by integrating the genetic algorithm; their wavelet network is used as an approximation tool to align the features of mesh to have efficient deformation processes. Also, such meshes are especially expensive to transmit and are awkward to deform. For this reason, they propose to use multiresolution analysis to decompose a surface geometry into several levels of detail in order to work only with the approximation coefficient at a chosen decomposition level. Hierarchical triangle mesh representations provide access to a triangle mesh at the desired resolution without omitting any information. The experimental results showed the validity of the generalisation ability and the efficiency of their suggested multi‐mother wavelet network architecture based on genetic algorithm and multiresolution analysis for 3D mesh modelling and deformation.

Artificial Intelligence 2D Image Deformation Technology for Digital Art

“…In their research on image warping and digital art, Dhibi et al proposed a new 3D mesh warping process using a multi-mother wavelet neural network architecture that relies on genetic algorithms and multi-resolution analysis [2]. Deficiencies and local minima are prevented by integrating genetic algorithms.…”