Multimedia Character Modeling Design and Modeling of Cartoon Animation Based on Bayesian Sequence Recommendation Algorithm

Wu, Hao; Wen, Shi-Jiang; Yang, Jonghoon

doi:10.1155/2022/9205227

Cited by 2 publications

(2 citation statements)

References 19 publications

(20 reference statements)

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“…The parametric method of mapping from one surface to another known area was first used for texture mapping. The parameterization process of this paper is based on the idea of the literature [ 10 ]. The coordinate mean method is used to express the coordinates of each two-dimensional point as a convex combination of its neighbor points, which is a method of harmonic mapping, which has both length and angle maintained to a certain extent [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Animation Interaction Design Algorithm Based on Example and Parameterization

Wang

Han

Yang

2022

Computational Intelligence and Neuroscience

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Intelligent design for the animation interaction is very challenging and needs to be resolved on priority basis. If this design is based on smart learning based mechanism, then it will be more appropriate and effective. Therefore, based on the idea of process modeling, the concrete method is based on example modeling and parameterization, and the VC programming environment is the tool. Animated interactions are not counted. The animation parameterization in the sample animation software and its attribute information are calculated, and the principle and realization process of the new animation interaction design are given, as well as the calculation method and steps of each area of animation elements. The same interaction design pattern can be filled in different areas by using parametric methods. In practice, we have increased the diversity of animation interaction design. The method proposed in this paper can quickly and automatically generate a virtual animation design network according to the user's design ideas and can be controlled by the user in real time interactively, providing a good interface and a new way for the user to design animation environment modeling.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Animation Interaction Design Algorithm Based on Example and Parameterization

Wang

Han

Yang

2022

Computational Intelligence and Neuroscience

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“…Wu et al [26] have presented Multimedia Character Modeling Design and Modeling of Cartoon Animation Depend on Bayesian Sequence Recommendation Process. It deliberates how cartoon animation for multimedia needs to project positive energy and conform to mainstream ideals.…”

Section: Literature Reviewmentioning

confidence: 99%

Artistic Creation and Animation Scene Designing using Hierarchical Spatio-Temporal Graph Convolutional Neural Network Optimized with Gazelle Optimization Algorithm

Hansi Fu

2024

jes

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Creating moving visuals, usually in the form of 2D or 3D animations, is the focus of the graphic design field known as animation design. A sequence of consecutive images, or frames, is played quickly one after the other in animation design to provide the impression of motion. In this Manuscript, Artistic Creation and Animation Scene Designing using Hierarchical Spatio-temporal graph convolutional neural network Optimized with Gazelle Optimization (AC-ASD-HSTCNN-GO)is proposed. Initially, data is taken from LiDAR dataset then the data are fed to preprocessing segment. For pre-processing Orthogonal Master Slave Adaptive Notch Filter (OMSANF) is used to remove duplicate data and replacing missing data from collected data’s. Then the feature are extracted by using General Synchroextracting Chirplet Transform (GSCT) technique, which extracts features such as objects, faces and scenes. Finally HSTGCNN for creating 3D animation image design .In general the HSTGCNN does not express some adaption of optimization strategies for determining optimum parameters to assure the product recommendation. Hence, the GOA is proposed to enhance HSTGCNN.The proposed AC-ASD-HSTCNN-GO method is activated in python and the performance of proposed AC-ASD-HSTCNN-GO is estimated under performance metrics like accuracy, precision, F1-score, recall, specificity, computational time, error rate, RoC. Finally, performance of AC-ASD-HSTCNN-GO method provides 15.27%, 17.14%, 18.58% greater accuracy, 16.11%, 19.65%, 20.53% greater precision and 19.31%, 21.29%, 22.15% greater F1-Score while compared with existing techniques like Graphic Design of 3D Animation Scenes Depend on Deep Learning with Information Security Technology (GD-3DASDL-IST), GRADE: Generating Realistic Animated Dynamic Environments for Robotics Research (GRADE-RR)andApplication of Random Forest Algorithm in Natural Landscape Animation Design (ALN-RFA-NLAD) respectively.

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Multimedia Character Modeling Design and Modeling of Cartoon Animation Based on Bayesian Sequence Recommendation Algorithm

Cited by 2 publications

References 19 publications

An Intelligent Animation Interaction Design Algorithm Based on Example and Parameterization

An Intelligent Animation Interaction Design Algorithm Based on Example and Parameterization

Artistic Creation and Animation Scene Designing using Hierarchical Spatio-Temporal Graph Convolutional Neural Network Optimized with Gazelle Optimization Algorithm

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