69 citations · 162 across the 9 of their papers we have counts for
11 papers
Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge
Achraf Ben-Hamadou, Nour Neifar, Ahmed Rekik +23
Teeth landmark detection is a key task in modern orthodontics, supporting advanced diagnosis, personalized treatment planning, and effective monitoring of treatment progress. Howev…
From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans
Marilyn Keller, Keenon Werling, Soyong Shin +4
Great progress has been made in estimating 3D human pose and shape from images and video by training neural networks to directly regress the parameters of parametric human models l…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
Matthieu Armando, Laurence Boissieux, Edmond Boyer +11
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain…
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik +29
Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-ba…
Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis
Achraf Ben-Hamadou, Nour Neifar, Ahmed Rekik +5
Intraoral 3D scanning is now widely adopted in modern dentistry and plays a central role in supporting key tasks such as tooth segmentation, detection, labeling, and dental landmar…
OSSO: Obtaining Skeletal Shape from Outside
Marilyn Keller, Silvia Zuffi, Michael J. Black +1
We address the problem of inferring the anatomic skeleton of a person, in an arbitrary pose, from the 3D surface of the body; i.e. we predict the inside (bones) from the outside (s…
Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle
Di Meng, Edmond Boyer, Sergi Pujades
Vertebrae localization, segmentation and identification in CT images is key to numerous clinical applications. While deep learning strategies have brought to this field significant…
Learning to Dress 3D People in Generative Clothing
Qianli Ma, Jinlong Yang, Anurag Ranjan +4
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not…
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
Nikolas Hesse, Sergi Pujades, Michael J. Black +3
Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and ar…
Detailed, accurate, human shape estimation from clothed 3D scan sequences
Chao Zhang, Sergi Pujades, Michael Black +1
We address the problem of estimating human pose and body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual tr…