Dynamic Style Generation of Clothing Based on Reinforcement Learning

被引：0
|
作者：
Jiang Z. [1 ]
Qian J. [2 ]
机构：
[1] Fashion Institute, Shaanxi Fashion Engineering University, Shaanxi, Xi'an
[2] College of Textile and Clothing, Xinjiang University, Urumqi
来源：
Computer-Aided Design and Applications | 2024年 / 21卷 / S23期
关键词：
Clothing Design; Computer-Aided Design; Dynamic Style Generation of Clothing; Reinforcement Learning;
D O I：
10.14733/cadaps.2024.S23.159-174
中图分类号：
学科分类号：
摘要：
This article aims to establish and validate a dynamic model for clothing style generation in order to enhance rapid style innovation and personalized customization in the clothing design sector. This study utilizes a clothing model grounded in CAD (Computer Aided Design) technology, paired with an RL (Reinforcement Learning) algorithm for style generation. By compiling and analyzing a comprehensive dataset of clothing CAD information and style reference samples, a simulation environment is created for model training and evaluation. The findings reveal that, in comparison to conventional CAD design techniques and rule-based style generation methods, the dynamic clothing style generation model presented in this study exhibits superior style consistency, originality, and aesthetic appeal. This model is capable of producing tailored clothing designs based on specified design elements and style references, demonstrating high levels of flexibility and adaptability. In conclusion, this research introduces an innovative design tool and model for the clothing industry, poised to streamline design processes, minimize costs, and foster sustainable industry growth. © 2024 U-turn Press LLC.
引用
收藏
页码：159 / 174
页数：15
相关论文
共 50 条
  • [1] Autoimmune Dynamic Attack Generation Method Based on Reinforcement Learning
    Li T.
    Tang Z.-L.
    Ma Z.
    Ma J.-F.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2023, 51 (11): : 3033 - 3041
  • [2] Reinforcement Learning Based Architectures for Dynamic Generation of Smart Home Services
    Qiu, Mingming
    Najm, Elie
    Sharrock, Remi
    Traverson, Bruno
    2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, 2022, : 7 - 14
  • [3] A dynamic checkpointing scheme based on reinforcement learning
    Okamura, H
    Nishimura, Y
    Dohi, T
    10TH IEEE PACIFIC RIM INTERNATIONAL SYMPOSIUM ON DEPENDABLE COMPUTING, PROCEEDINGS, 2004, : 151 - 158
  • [4] A reinforcement learning based dynamic walking control
    Mao, Yong
    Wang, Jiaxin
    Ha, Peifa
    Li, Shi
    Qiu, Zhen
    Zhang, Le
    Han, Zhuo
    PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10, 2007, : 3609 - +
  • [5] Graph learning-based generation of abstractions for reinforcement learning
    Xue, Yuan
    Kudenko, Daniel
    Khosla, Megha
    NEURAL COMPUTING & APPLICATIONS, 2023,
  • [6] Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control
    Liu, Xin-Yang
    Wang, Jian-Xun
    PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2021, 477 (2255):
  • [7] Infrared Adversarial Patch Generation Based on Reinforcement Learning
    Zhou, Shuangju
    Li, Yang
    Tan, Wenyi
    Zhao, Chenxing
    Zhou, Xin
    Pan, Quan
    MATHEMATICS, 2024, 12 (21)
  • [8] Detecting Domain Generation Algorithms Based on Reinforcement Learning
    Cheng Hua
    Fang Yiquan
    Chen Lihuang
    Cai Jing
    2019 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC), 2019, : 261 - 264
  • [9] A dynamic route guidance arithmetic based on reinforcement learning
    Zhang, Z
    Xu, JM
    PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, : 3607 - 3611
  • [10] Explicit Dynamic Coordination Reinforcement Learning Based on Utility
    Si, Huaiwei
    Tan, Guozhen
    Yuan, Yifu
    Peng, Yanfei
    Li, Jianping
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2022, 16 (03): : 792 - 812
12345