Learning a shared deformation space for efficient design-preserving garment transfer

Shi, Min; Wei, Yukun; Chen, Lan; Zhu, Daiyin; Mao, Tianlu; Wang, Zhaoqi

doi:10.1016/j.gmod.2021.101106

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The modern model of garment design consists mainly of style design, structure design, and process design. The main task of style design is to draw up renderings and style drawings, the main task of structural design is to development patterns, and the main task of process design is to produce process guidance documents [ 7 , 8 ]. As shown in Figure 1 , style design, structure design, and process design are interlinked and they can form the main part of modern garment design.…”

Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

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With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises.

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Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

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“…The evolutionary algorithm is used to gradually search for individuals that meet user requirements in complex environments, seeking better solutions. However, methods on the ground of the genetic algorithm and manual scoring may lead to user fatigue, which can affect usage persistence and result accuracy after multiple iterations [17,18]. The study analyzes the accuracy of fatigue level assessment through comparative testing.…”

Section: Geomantic → {Con Env Sea}mentioning

confidence: 99%

Indoor Space Layout Design based on Differential Evolution Algorithm

Meng

2024

SCPE

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To enhance the interactivity of spatial design, this study proposes an indoor spatial layout design method based on differential evolution algorithm, which combines backtracking strategy and reverse learning strategy to improve the interactive differential evolution algorithm. The experimental data demonstrated that the proposed method for indoor space layout design achieved an average user satisfaction of 81.7%, which increased by 16.8% compared with traditional interactive genetic algorithms. In addition, the improved human-computer interaction interface scored higher than 0.8 in terms of usability, reliability, customizability, and interactive feedback. This means that the improved interface can better meet user needs and provide a better user experience. This study shows that the indoor space layout design method ground on differential evolution algorithm and the improved human-computer interaction interface can significantly improve user satisfaction and user experience. This has brought more efficient and convenient solutions to the field of spatial design.

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“…With the development of the social economy, people are no longer simply satisfied with traditional material life, and their pursuit of clothing has gradually become diversified [1,2]. Clothing has changed from traditional Chinese tunic suits and bragis to existing products with cultural and geographical elements, with more diversified connotations [3,4].…”

Section: Introductionmentioning

confidence: 99%

The CAD Digital Automation Analysis of Costume Designing Based on Immersive Virtual Reality Models

Zheng

2022

Advances in Multimedia

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With the development of the social economy, costume design is becoming more and more trendy and diversified, which can better reflect cultural and geographical elements. However, traditional costume design often causes excessive waste of resources and time, which is an issue worthy of attention and value. In view of these limitations, an immersive virtual reality model is introduced in this paper. Digitization is realized based on CAD through combing the business logic involved in the costume design process, and virtual fitting and virtual sewing are realized according to virtual reality, and the effect of virtual dressing is viewed and adjusted according to the corresponding feedback, saving cost and time. The simulation experiment research shows that the immersive virtual reality model is effective and can effectively achieve the effective improvement of intrinsic motivation and self-efficacy. Meanwhile, it has a good effect in the assessment of costume design learning transfer and can effectively support the analysis of CAD digital automation.

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Learning a shared deformation space for efficient design-preserving garment transfer

Cited by 7 publications

References 35 publications

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Indoor Space Layout Design based on Differential Evolution Algorithm

The CAD Digital Automation Analysis of Costume Designing Based on Immersive Virtual Reality Models

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