BODYFITR: Robust Automatic 3D Human Body Fitting

This paper proposes BODYFITR, a fully automatic method to fit a human body model to static 3D scans with complex poses. Automatic and reliable 3D human body fitting is necessary for many applications related to healthcare, digital ergonomics, avatar creation and security, especially in industrial contexts for large-scale product design. Existing works either make prior assumptions on the pose, require manual annotation of the data or have difficulty handling complex poses. This work addresses these limitations by providing a novel automatic fitting pipeline with carefully integrated building blocks designed for a systematic and robust approach. It is validated on the 3DBodyTex dataset, with hundreds of high-quality 3D body scans, and shown to outperform prior works in static body pose and shape estimation, qualitatively and quantitatively. The method is also applied to the creation of realistic 3D avatars from the high-quality texture scans of 3DBodyTex, further demonstrating its capabilities

Towards Automatic Human Body Model Fitting to a 3D Scan

This paper presents a method to automatically recover a realistic and accurate body shape of a person wearing clothing from a 3D scan. Indeed, in many practical situations, people are scanned wearing clothing. The underlying body shape is thus partially or completely occluded. Yet, it is very desirable to recover the shape of a covered body as it provides non-invasive means of measuring and analysing it. This is particularly convenient for patients in medical applications, customers in a retail shop, as well as in security applications where suspicious objects under clothing are to be detected. To recover the body shape from the 3D scan of a person in any pose, a human body model is usually fitted to the scan. Current methods rely on the manual placement of markers on the body to identify anatomical locations and guide the pose fitting. The markers are either physically placed on the body before scanning or placed in software as a postprocessing step. Some other methods detect key points on the scan using 3D feature descriptors to automate the placement of markers. They usually require a large database of 3D scans. We propose to automatically estimate the body pose of a person from a 3D mesh acquired by standard 3D body scanners, with or without texture. To fit a human model to the scan, we use joint locations as anchors. These are detected from multiple 2D views using a conventional body joint detector working on images. In contrast to existing approaches, the proposed method is fully automatic, and takes advantage of the robustness of state-of-art 2D joint detectors. The proposed approach is validated on scans of people in different poses wearing garments of various thicknesses and on scans of one person in multiple poses with known ground truth wearing close-fitting clothing

3DBodyTex: Textured 3D Body Dataset

In this paper, a dataset, named 3DBodyTex, of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan. 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry. It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones. As the main output of these sensors is the 3D shape, many methods rely on this information alone. The 3D shape information is, however, very high dimensional and leads to models that must handle many degrees of freedom from limited information. Coupling texture and 3D shape alleviates this burden, as the texture of 3D objects is complementary to their shape. Unfortunately, high-quality texture content is lacking from commonly available datasets, and in particular in datasets of 3D body scans. The proposed 3DBodyTex dataset aims to fill this gap with hundreds of high-quality 3D body scans with high-resolution texture. Moreover, a novel fully automatic pipeline to fit a body model to a 3D scan is proposed. It includes a robust 3D landmark estimator that takes advantage of the high-resolution texture of 3DBodyTex. The pipeline is applied to the scans, and the results are reported and discussed, showcasing the diversity of the features in the dataset

Characterization, antibacterial, antioxidant, antidiabetic, and anti-inflammatory activities of green synthesized silver nanoparticles using Phragmanthera austroarabica A. G. Mill and J. A. Nyberg extract

IntroductionDiabetes mellitus is a chronic metabolic disorder that exhibited great expansion all over the world. It is becoming an epidemic disease adding a major burden to the health care system, particularly in developing countries.MethodsThe plant under investigation in the current study Phragmanthera austroarabica A. G. Mill and J. A. Nyberg is traditionally used in Saudi Arabia for the treatment of diabetes mellitus. The methanolic extract (200 mg/kg) of the plant and pure gallic acid (40 mg/kg), a major metabolite of the plant, as well as their silver nanoparticle formulae (AgNPs) were evaluated for their antidiabetic activity.Results and DiscussionThe results showed a decrease in body fat, obesity, an improvement in lipid profiles, normalization of hyperglycemia, insulin resistance, and hyperinsulinemia, and an improvement in liver tissue structure and function. However, the results obtained from AgNPs for both extract and the pure gallic acid were better in most measured parameters. Additionally, the activity of both the crude extract of the plant and its AgNPs were evaluated against a number of gram-positive, gram-negative bacteria and fungi. Although the activity of the crude extract ranged from moderate to weak or even non-active, the AgNPs of the plant extract clearly enhanced the antimicrobial activity. AgNPs of the extract demonstrated remarkable activity, especially against the Gram-negative pathogens Proteus vulgaris (MIC 2.5 μg/ml) and Pseudomonas aeruginosa (MIC 5 μg/ml). Furthermore, a promising antimicrobial activity was shown against the Gram-positive pathogen Streptococcus mutants (MIC 1.25 μg/ml)