Learning to Estimate the Body Shape Under Clothing From a Single 3-D Scan

Hu, Pengpeng; Kaashki, Nastaran Nourbakhsh; Dadarlat, Vasile; Munteanu, Adrian

doi:10.1109/tii.2020.3016591

Cited by 32 publications

(37 citation statements)

References 34 publications

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“…Early works fitted the body template into the dressed body model [25][26] [27] by solving complex optimization problems. [28] proposed the first deep learning method, Body PointNet, for estimating body shape under clothing from 3D data. However, it requires a complete dressed body scan as input, which is not always available.…”

Section: Body Shape Under Clothingmentioning

confidence: 99%

“…Other types of clothing can be easily prepared using the same method. This strategy is also applied in [28], which is the state-of-the-art in estimating body shape under clothing from a 3D scan. One notes that the average time of putting clothes on one SMPL body is only 0.6 seconds.…”

Section: Datasetmentioning

confidence: 99%

“…One notes that the average time of putting clothes on one SMPL body is only 0.6 seconds. This enables generating large training datasets with a broad range of body morphologies for various categories of clothing; for more details, the interested reader is referred to [28].…”

Section: Datasetmentioning

confidence: 99%

“…To quantitatively compare our algorithm with existing stateof-the-art methods, we perform experiments using the BUG dataset [28] as it has the ground-truth body shapes and dressed bodies. We compare our method against various state-of-theart methods including that of [2] that uses two depth-images as input, the single depth-image based method of [38], and the single RGB image based method of [47].…”

Section: Quantitative Comparisonmentioning

confidence: 99%

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3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape From a Single Commodity Depth Camera

Munteanu

2022

IEEE Trans. Multimedia

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Knowledge about individual body shape has numerous applications in various domains such as healthcare, fashion and personalized entertainment. Most of the depth based whole body scanners need multiple cameras surrounding the user and requiring the user to keep a canonical pose strictly during capturing depth images. These scanning devices are expensive and need professional knowledge for operation. In order to make 3D scanning as easy-to-use and fast as possible, there is a great demand to simplify the process and to reduce the hardware requirements. In this paper, we propose a deep learning algorithm, dubbed 3DBodyNet, to rapidly reconstruct the 3D shape of human bodies using a single commodity depth camera. As easy-to-use as taking a photo using a mobile phone, our algorithm only needs two depth images of the front-facing and back-facing bodies. The proposed algorithm has strong operability since it is insensitive to the pose and the pose variations between the two depth images. It can also reconstruct an accurate body shape for users under tight/loose clothing. Another advantage of our method is the ability to generate an animatable human body model. Extensive experimental results show that the proposed method enables robust and easy-to-use animatable human body reconstruction, and outperforms the state-of-the-art methods with respect to running time and accuracy.

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Section: Body Shape Under Clothingmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Quantitative Comparisonmentioning

confidence: 99%

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3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape From a Single Commodity Depth Camera

Munteanu

2022

IEEE Trans. Multimedia

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“…Especially, SURF feature detection, the bag-of-features model, and machine learning technique have not been proposed to differentiate the body shapes for garment fit. The conventional method for body shape detection is limited to a physical measurement-based approach [20] or a multiple photo-based 3D reconstruction approach [15], which are mostly inaccurate and impractical due to the expensive computation [21]. Therefore, in this paper, we suggest a unique combination of image processing and machine learning based body shape detection using 2D smartphone images.…”

Section: Introductionmentioning

confidence: 99%

SmartFit: Smartphone Application for Garment Fit Detection

et al. 2021

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The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market.

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A methodology for realistic human shape reconstruction from 2D images

Curbelo,

Spiteri

2024

Multimed Tools Appl

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Learning to Estimate the Body Shape Under Clothing From a Single 3-D Scan

Cited by 32 publications

References 34 publications

3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape From a Single Commodity Depth Camera

3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape From a Single Commodity Depth Camera

SmartFit: Smartphone Application for Garment Fit Detection

A methodology for realistic human shape reconstruction from 2D images

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