References of "Saint, Alexandre Fabian A 50026056"
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See detailSHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Saint, Alexandre Fabian A UL; Kacem, Anis UL; Cherenkova, Kseniya UL et al

Scientific Conference (2020, August 23)

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw ... [more ▼]

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organized as a workshop in conjunction with ECCV 2020. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. Challenge 1 is further split into two tracks, focusing, first, on large body and clothing regions, and, second, on fine body details. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction, and the amount of completed data. Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks. The datasets are released to the scientific community. Moreover, an accompanying custom library of software routines is also released to the scientific community. It allows for processing 3D scans, generating partial data and performing the evaluation. Results of the competition, analyzed in comparison to baselines, show the validity of the proposed evaluation metrics and highlight the challenging aspects of the task and of the datasets. Details on the SHARP 2020 challenge can be found at https://cvi2.uni.lu/sharp2020/ [less ▲]

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See detail3DBooSTeR: 3D Body Shape and Texture Recovery
Saint, Alexandre Fabian A UL; Kacem, Anis UL; Cherenkova, Kseniya UL et al

Scientific Conference (2020, August 23)

We propose 3DBooSTeR, a novel method to recover a textured 3D body mesh from a textured partial 3D scan. With the advent of virtual and augmented reality, there is a demand for creating realistic and high ... [more ▼]

We propose 3DBooSTeR, a novel method to recover a textured 3D body mesh from a textured partial 3D scan. With the advent of virtual and augmented reality, there is a demand for creating realistic and high-fidelity digital 3D human representations. However, 3D scanning systems can only capture the 3D human body shape up to some level of defects due to its complexity, including occlusion between bodyparts, varying levels of details, shape deformations and the articulated skeleton. Textured 3D mesh completion is thus important to enhance3D acquisitions. The proposed approach decouples the shape and texture completion into two sequential tasks. The shape is recovered by an encoder-decoder network deforming a template body mesh. The texture is subsequently obtained by projecting the partial texture onto the template mesh before inpainting the corresponding texture map with a novel approach. The approach is validated on the 3DBodyTex.v2 dataset [less ▲]

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See detailTowards Generalization of 3D Human Pose Estimation In The Wild
Baptista, Renato UL; Saint, Alexandre Fabian A UL; Al Ismaeil, Kassem UL et al

in International Conference on Pattern Recognition (ICPR) Workshop on 3D Human Understanding, Milan 10-15 January 2021 (2020)

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate ... [more ▼]

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate datasets. Existent ones are usually collected in indoor controlled environments where motion capture systems are used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations. These images are generated from 200 viewpoints among which 70 challenging extreme viewpoints. This data was created starting from high resolution textured 3D body scans and by incorporating various realistic backgrounds. Retraining a state-of-the-art 3D pose estimation approach using data augmented with 3DBodyTex.Pose showed promising improvement in the overall performance, and a sensible decrease in the per joint position error when testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the research community with new possibilities for generalizing 3D pose estimation from monocular in-the-wild images. [less ▲]

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See detailBODYFITR: Robust Automatic 3D Human Body Fitting
Saint, Alexandre Fabian A UL; Shabayek, Abd El Rahman UL; Cherenkova, Kseniya UL et al

in Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP) (2019, September 22)

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 ... [more ▼]

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. [less ▲]

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See detail3DBodyTex: Textured 3D Body Dataset
Saint, Alexandre Fabian A UL; Ahmed, Eman UL; Shabayek, Abd El Rahman UL et al

in 2018 Sixth International Conference on 3D Vision (3DV 2018) (2018)

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 ... [more ▼]

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. [less ▲]

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See detailTowards Automatic Human Body Model Fitting to a 3D Scan
Saint, Alexandre Fabian A UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in D'APUZZO, Nicola (Ed.) Proceedings of 3DBODY.TECH 2017 - 8th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017 (2017, October)

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 ... [more ▼]

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. [less ▲]

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See detailDEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS
Shabayek, Abd El Rahman UL; Aouada, Djamila UL; Saint, Alexandre Fabian A UL et al

in IEEE International Conference on Image Processing, Beijing 17-20 Spetember 2017 (2017)

Detailed reference viewed: 372 (60 UL)