Feature extraction method of 3D art creation based on deep learning

Chen, Kaiqing; Huang, Xiaoqin

doi:10.1007/s00500-019-04353-0

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…This distribution of the oxidation zone has been observed in many other field measurements and simulations (Xia et al, 2015;Xu et al, 2020). Coal near the working face undergoes a strong oxidation reaction because of the abundant O 2 , accompanied by the release of heat.…”

Section: Analysis Of the Oxidation Zone Without Co 2 Injectionsupporting

confidence: 81%

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Coal and Rock Dynamic Disasters: Advances of Physical and Numerical Simulation in Monitoring, Early Warning, and Prevention - Volume II

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Analysis Of the Oxidation Zone Without Co 2 Injectionsupporting

confidence: 81%

“…Therefore, it is a significant challenge to build mathematical models with reasonable permeability and porosity in the gob area. According to the observation of the mine pressure, the compaction bulking factor, k, of the gob follows the law (Xia et al, 2015):…”

Section: Numerical Simulationmentioning

confidence: 99%

Coal and Rock Dynamic Disasters: Advances of Physical and Numerical Simulation in Monitoring, Early Warning, and Prevention - Volume II

2024

Frontiers Research Topics

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“…Jiang et al proposed an effective deep learning model based on the convolutional neural network, which was used for 3D underground mapping of 2D ground observation data to realize 3D address feature construction [ 11 ]. Chen and Huang have studied the feature extraction of the 3D art design model based on deep learning, proposed a 3D art network social communication method, and verified the feasibility of this method [ 12 ]. Zhang studied the application of the dominant color extraction algorithm in clothing image retrieval and proposed a main clothing image color feature extraction algorithm to extract the main colors in the clothing image [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Liu

Ren

Qin³

2021

Computational Intelligence and Neuroscience

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In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product. However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation. In view of this, this paper proposes a 3D clothing color display model based on deep learning to support human modeling-driven. Firstly, the macro-micro adversarial network (MMAN) based on deep learning is used to analyze the original image, and then, the results are preprocessed. Finally, the 3D model with the original image color is constructed by using UV mapping. The experimental results show that the accuracy of the MMAN algorithm reaches 0.972, the established three-dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points. The above results show that it is effective to use deep learning to build a 3D model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

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“…It uses the concept of biological evolution and variation to modify parameter values and uses iterative evolution to obtain satisfactory results [3]. It can be used as an inspiration for opera art creation [4,5]. Combining the two algorithms to analyze the process of opera art creation, we can get the relevant opera creation data.…”

Section: Introductionmentioning

confidence: 99%

Creation of Drama Art Based on Deep Learning and Evolution Strategy

Tang

2022

Scientific Programming

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The rapid development of information technology has promoted the growth of deep learning, artificial intelligence, and big data technology. Nowadays, the artistic form of traditional opera creation is accepted and respected by people—more and more people like the art form. However, the artistic creation of traditional opera needs inspiration. The essence of inspiration creation is to reconstruct its objective structure. The deep learning algorithm’s essence is to extract all of the attributes of sample self-learning input data and use them as inspiration for artistic production. First, this paper briefly introduces the deep learning and evolution strategy and uses these algorithms in opera art creation to construct 1 + λ. With the help of this evolutionary algorithm, an optimal solution is obtained through random evolution. The evolution strategy establishes the evolution function matrix. Starting from the situation of students learning opera art, this article examines the process of creating opera art using an in-depth learning and evolution technique. The results show that 96 percent of the students have contact with opera while watching an opera tour. During this, they were not interested in the performance of literary drama in traditional opera. However, it was noticed that they were deeply interested in martial arts, clown performances, and drama stage performances. Finally, the audience group of opera artistic creation is analyzed in the form of opera animation of “A Journey to the West: The Return of the Great Sage.” It signifies that the opera’s leading audience group is aged 25 to 29. However, they account for only 30 percent.

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Feature extraction method of 3D art creation based on deep learning

Cited by 8 publications

References 34 publications

Coal and Rock Dynamic Disasters: Advances of Physical and Numerical Simulation in Monitoring, Early Warning, and Prevention - Volume II

Coal and Rock Dynamic Disasters: Advances of Physical and Numerical Simulation in Monitoring, Early Warning, and Prevention - Volume II

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Creation of Drama Art Based on Deep Learning and Evolution Strategy

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