Fast Adaptive Character Animation Synthesis Based on Greedy Algorithm

Abstract

On the premise of ensuring the animation effect and real-time performance, it is of great significance and value for large-scale group character animation synthesis how to reduce the disaster coincidence degree among various models of fast adaptive character animation synthesis. The realization method of object-oriented finite state machine is studied in detail. Finite state machine (FSM) is an efficient behavior modeling method, which can describe the behavioral decisions of fast adaptive character animation synthesis in a complex virtual environment. Based on the implementation defects of the finite state machine in the traditional structure, using object-oriented thinking, combined with the state design mode, we further studied a finite state machine implementation method based on object-oriented technology. This achieves code reuse and simple program maintenance. The effect is extensible and effectively overcomes the shortcomings of traditional character animation synthesis. Secondly, the multipath matching tracking algorithm of the greedy algorithm is studied to generate multiple candidate sets through multiple paths, and finally, the candidate set with the minimum residual error is selected as the estimated support set, so as to improve the reconstruction performance. Further, based on the idea of multipath, using the regularization method of the ROMP algorithm, the regularized multipath matching tracking RMSP algorithm is proposed. It uses the regularized subset method to generate multiple paths and chooses the path with the fastest residual reduction as the support set of this iteration. The simulation results show that the RMSP algorithm has better reconstruction performance than the SP algorithm.

Color Matching Generation Algorithm for Animation Characters Based on Convolutional Neural Network

“…Subsequently, G z is fed to the discriminator D together with x for discriminating. For the discriminator D that is used to discriminate, the value of D G(Z) is close to 0, while the value of D X is close to 1; and with the true and false images, the value of the discriminator is used to reflect D, given G with to update the parameters [13]. e VGG model group (Oxford Visual Geometry Group) was originally used as a model for image feature classification systems, but the convolutional neural network part of the model has since been proven by scientists to provide a good technical solution for image feature classification extraction.…”

“…Subsequently, G z is fed to the discriminator D together with x for discriminating. For the discriminator D that is used to discriminate, the value of D G ( Z ) is close to 0, while the value of D X is close to 1; and with the true and false images, the value of the discriminator is used to reflect D , given G with to update the parameters [ 13 ].…”

Perbandingan Algoritma Welch Powell dan Algoritma Greedy dalam Optimasi Penjadwalan Ruang Kuliah Semester Genap Fakultas Teknik

“…Secara umum, algoritma ini bekerja dengan cara mengurutkan terlebih dahulu derajat setiap simpul, kemudian menambahkan satu simpul baru di setiap langkahnya [14]. Penambahan pada setiap simpul baru ini dilakukan dengan mengambil simpul dengan derajat terkecil yang dimasukkan ke dalam graf [15]. Algoritma ini banyak digunakan pada setiap permasalahan karena strateginya yang intuitif serta efisiensi kinerja yang cukup tinggi [16].…”

A Sparse Recovery Algorithm Based on Arithmetic Optimization

“…The position update of the operator in the AOA algorithm is carried out through its own experience and the neighborhood experience. In this paper, the algorithm AOA-CS is based on the population evolution strategy and process, and incorporates a random component to perform location updates [22][23][24][25][26]. Each time the operator starts searching, it first updates MOA and MOP values, and then generates three random numbers r1, r2 and r3 that obey uniform distribution between 0 and 1.…”