Estimating 3D human shape under clothing from a single RGB image

Shigeki, Yui; Okura, Fumio; Mitsugami, Ikuhisa; Yagi, Yasushi

doi:10.1186/s41074-018-0052-9

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…However, regarding estimating human body shape, this approach could not produce an acceptable result. Different from the above approaches, Shigeki et al, (2018) combined the optimization-based method with a regression-based method to form a new one called SPIN (SMPL optimization IN the loop). The objective is to initialize an iterative optimization from a regressed estimate of the network that speeds up the fitting process and leads the optimization to more accurate model fits compared to SMPLify (Bogo et al, 2016) or HMR (Kanazawa et al, 2018).…”

Section: Recentmentioning

confidence: 99%

Hybrid optimization in loop: 3D human body reconstruction from basic information

al.¹

2022

Int. j. adv. appl. sci.

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3D human reconstruction is widely used for digital transformation in different industries such as e-retail, entertainment, health care, epidemiology. For practical applicability, the modeling process is required to be quick, affordable while still ensuring capabilities in reconstruction accuracy and reliability. To meet such business requirements, we propose a novel technique for producing an exact 3D human body using only basic anthropomorphic measurements. To begin, the paper refers to and summarizes core technologies of the three most common 3D human reconstruction approaches, including (1) Using Point Clouds, (2) Using Images, and (3) Using Anthropometric Measurements. Despite successfully recreating 3D human shapes, these methods face problems of long processing time and high investment cost, making the solution impractical for mass use. Moreover, in the human reconstruction sector particularly, the variety of human shapes, poses, and clothing poses a significant challenge to output accuracy. In this regard, our method combines (1) a local optimization model for determining hyperparameters for classifying different human shapes and (2) a global optimization for reconstructing 3D models, allowing reconstruction of both naked human bodies and clothed ones. The proposed method was evaluated quantitatively and qualitatively using a real dataset to demonstrate its feasibility and efficiency when used in real-world applications.

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Section: Recentmentioning

confidence: 99%

Hybrid optimization in loop: 3D human body reconstruction from basic information

al.¹

2022

Int. j. adv. appl. sci.

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“…Methods inferring from 2D input images were proposed to estimate the body measurements from visual data to avoid the need for manual measuring in deployment. Most frequently, the input data are in a form of RGB images (Yan and Kämäräinen, 2021;Anisuzzaman et al, 2019;Shigeki et al, 2018), although the three color channels may not be very beneficial in context of this particular task, at the cost of processing the three-channeled data. Thus, several other approaches settled for gray-scale images (Tejeda and Mayer, 2021) as input data, while achieving competitive results.…”

Section: Image Input Datamentioning

confidence: 99%

Automatic Estimation of Anthropometric Human Body Measurements

Škorvánková¹,

Riečický²,

Madaras³

2021

Preprint

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Research tasks related to human body analysis have been drawing a lot of attention in computer vision area over the last few decades, considering its potential benefits on our day-to-day life. Anthropometry is a field defining physical measures of a human body size, form, and functional capacities. Specifically, the accurate estimation of anthropometric body measurements from visual human body data is one of the challenging problems, where the solution would ease many different areas of applications, including ergonomics, garment manufacturing, etc. This paper formulates a research in the field of deep learning and neural networks, to tackle the challenge of body measurements estimation from various types of visual input data (such as 2D images or 3D point clouds). Also, we deal with the lack of real human data annotated with ground truth body measurements required for training and evaluation, by generating a synthetic dataset of various human body shapes and performing a skeleton-driven annotation.

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LGVTON: a landmark guided approach for model to person virtual try-on

Roy

Santra

Chanda

2022

Multimed Tools Appl

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The system of Virtual Try-ON (VTON) allows a user to try a product virtually. In general, a VTON system takes a clothing source and a person's image to predict the try-on output of the person in the given clothing. Although existing methods perform well for simple poses, in case of bent or crossed arms posture or when there is a significant difference between the alignment of the source clothing and the pose of the target person, these methods fail by generating inaccurate clothing deformations. In the VTON methods that employ Thin Plate Spline (TPS) based clothing transformations, this mainly occurs for two reasons -(1) the second-order smoothness constraint of TPS that restricts the bending of the object plane. (2) Overlaps among different clothing parts (e.g., sleeves and torso) can not be modeled by a single TPS transformation, as it assumes the clothing as a single planar object; therefore, disregards the independence of movement of different clothing parts. To this end, we make two major contributions. Concerning the bending limitations of TPS, we propose a human AnaTomy-Aware Geometric (ATAG) transformation. Regarding the overlap issue, we propose a part-based warping approach that divides the clothing into independently warpable parts to warp them separately and later combine them. Extensive analysis shows the efficacy of this approach.

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Estimating 3D human shape under clothing from a single RGB image

Cited by 7 publications

References 13 publications

Hybrid optimization in loop: 3D human body reconstruction from basic information

Hybrid optimization in loop: 3D human body reconstruction from basic information

Automatic Estimation of Anthropometric Human Body Measurements

LGVTON: a landmark guided approach for model to person virtual try-on

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