Modeling Realistic Clothing From a Single Image Under Normal Guide

Liu, Xinqi; Li, Jituo; Lu, Guodong

doi:10.1109/tvcg.2023.3245583

Cited by 3 publications

(3 citation statements)

References 69 publications

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“…The quantity of problem dimensions is known as dimensions. Maximum velocity is obtained in equation (18).…”

Section: E Optimization Using Sheep Flock Optimization Algorithm (Sfoa)mentioning

confidence: 99%

“…These drawbacks highlight the need for further refinement and consideration in the application of these methods. Clothing design lies in the style industry's potential impact on the surroundings, including issues related to fast fashion [18,19]. The rapid turnover of trends and the pressure to produce clothing at a high pace contribute to overconsumption, excessive waste, and environmental degradation [20].…”

Section: Introductionmentioning

confidence: 99%

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Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network

Guannan Yang

2024

jes

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Recommendations for clothing design styles provide a tailored and efficient buying experience, decreasing consumers' decision fatigue and boosting interest. There are a number of obstacles to implementing clothing design style recommendations, including the requirement for diverse and accurate data to train efficient algorithms, the possibility of biases in recommendation systems, the complexity of comprehending subtle customer preferences, and the ever-changing nature of fashion trends. To overcome this complication, Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network (ICMA-SPGAN-SFOA-CD) are proposed. Initially, the images collected from the clothing dataset are given as input. Afterward, the data are fed to pre-processing. In pre-processing, Reverse Image Filter (RIF) is used for redundant of data and noise removal. The pre-processing output is fed to Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) for clothing design style recommendation. The recommended clothing design style was given for classification using Semantic-Preserved Generative Adversarial Network (SPGAN) optimized with Sheep Flock Optimization Algorithm (SFOA)for classifying clothing design style as Basic clothing, Looseness Season for clothing, Collar type, Colour and Advanced clothing . MATLAB is used to implement the proposed ICMA-SPGAN-SFOA-CD method. The performance of the proposed ICMA-SPGAN-SFOA-CD approach attains 25%, 21.5%, and 22.5% high precision,25.3%, 27.5%, and 21.8% high recall and 27.6%, 24.8%, and 23.2% high F1-Scorecompared with existing methods such as RCNN Framework Using L-Softmax Loss (CAR-RCNN-LSL) ,Clothing Attribute Recognition Based on Clothing fashion style recognition with design issue graph (CFSR-DCNN-DIG), Personalized Smart Clothing Design Based on Multimodal Visual Data Detection (PSCD-DNN-MVDD) models respectively.

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“…The quantity of problem dimensions is known as dimensions. Maximum velocity is obtained in equation (18).…”

Section: E Optimization Using Sheep Flock Optimization Algorithm (Sfoa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network

Guannan Yang

2024

jes

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“…DeepGarment [9], MGN [5], and BCNet [19] train neural networks on synthetic images to predict the vertex positions of specific mesh templates. [7] and [32] leverage normal estimation to optimize the vertex position of the template and recover wrinkle details. Such methods are inherently limited in the range of shapes they can handle, and those being trained on synthetic data can easily fail when facing real images.…”

Section: Related Workmentioning

confidence: 99%

DIG: Draping Implicit Garment over the Human Body

Ren

Guillard

Remelli³

et al. 2023

Lecture Notes in Computer Science

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Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are draped. However, none of them can handle multi-layered clothing, which is prevalent in everyday dress. In this paper, we introduce a parametric garment representation model that can. As in models used by clothing designers, each garment consists of individual 2D panels. Their 2D shape is defined by a Signed Distance Function and 3D shape by a 2D to 3D mapping. The 2D parameterization enables easy detection of potential collisions and the 3D parameterization handles complex shapes effectively. We show that this combination is faster and yields higher quality reconstructions than purely implicit surface representations, and makes the recovery of layered garments from images possible thanks to its differentiability. Furthermore, it supports rapid editing of garment shapes and texture by modifying individual 2D panels.Preprint. Under review.

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