Search Results for author:
Found 11 papers, 7 papers with code
HIT: Estimating Internal Human Implicit Tissues from the Body Surface
We first create a dataset of human tissues by segmenting full-body MRI scans and registering the SMPL body mesh to the body surface.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions.
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge
To address these challenges, the 3DTeethSeg'22 challenge was organized in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans.
Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis
Intraoral 3D scans analysis is a fundamental aspect of Computer-Aided Dentistry (CAD) systems, playing a crucial role in various dental applications, including teeth segmentation, detection, labeling, and dental landmark identification.
OSSO: Obtaining Skeletal Shape from Outside
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 (skin).
Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle
Vertebrae localization, segmentation and identification in CT images is key to numerous clinical applications.
Learning to Dress 3D People in Generative Clothing
To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants.
Detailed, accurate, human shape estimation from clothed 3D scan sequences
We address the problem of estimating human pose and body shape from 3D scans over time.
Bayesian View Synthesis and Image-Based Rendering Principles
In this paper, we address the problem of synthesizing novel views from a set of input images.
