Three-dimensional morphable models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head parts: (i). use a regressor to complete missing parts of one model using the other, and (ii). use the Gaussian Process framework to blend covariance matrices from multiple models. Thus, we build a new combined face-and-head shape model that blends the variability and facial detail of an existing face model (the LSFM) with the full head modelling capability of an existing head model (the LYHM). Then we construct and fuse a highly-detailed ear model to extend the variation of the ear shape. Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity. The new model achieves state-of-the-art performance. We use our model to reconstruct full head representations from single, unconstrained images allowing us to parameterize craniofacial shape and texture, along with the ear shape, eye gaze and eye color.
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http://dx.doi.org/10.1109/TPAMI.2020.2991150 | DOI Listing |
Angew Chem Int Ed Engl
January 2025
Ritsumeikan University: Ritsumeikan Daigaku, Applied Chemistry, B805 Biolink, 1-1-1 Nojihigashi, 525-8577, Kusatsu, JAPAN.
Inorganic photochromic materials offer several advantages over organic compounds, including relatively inexpensive and higher thermal stability. However, tuning their color with the same component has remained a significant challenge. In this study, we demonstrate that the photochromic color of Cu-doped ZnS nanocrystals (NCs), which is initially pale yellow before light irradiation, can be tuned from gray to brown by adjusting the surface stoichiometry of Zn and S, which is controlled through the use of thiol and non-thiol ligands.
View Article and Find Full Text PDFInt J Surg
January 2025
Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Introduction: Lung function has been associated with cognitive decline and dementia, but the extent to which lung function impacts brain structural changes remains unclear. We aimed to investigate the association of lung function with structural macro- and micro-brain changes across mid- and late-life.
Methods: The study included a total of 37 164 neurologic disorder-free participants aged 40-70 years from the UK Biobank, who underwent brain MRI scans 9 years after baseline.
Int J Surg
January 2025
Department of General Surgery.
Objective: Gallstones have gradually become a highly prevalent digestive disease worldwide. This study aimed to investigate the association of nine different obesity-related indicators (BRI, RFM, BMI, WC, LAP, CMI, VAI, AIP, TyG) with gallstones and to compare their predictive properties for screening gallstones.
Methods: Data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) for the 2017-2020 cycle, and weighted logistic regression analyses with multi-model adjustment were conducted to explore the association of the nine indicators with gallstones.
Int J Surg
January 2025
Department of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Background: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes.
Materials And Methods: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study.
Int J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
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