Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/Comment: 15 pages, 15 figure

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about the fetus through landmark connections, thus benefiting various critical applications, such as biometric measurements, plane localization, and fetal movement monitoring. However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses. In this study, we propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. Our contribution is three-fold. First, we propose a heuristic scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches, which can enlarge the input image resolution for better results under limited GPU memory. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. It separates the hidden classification task from the landmark localization task and thus progressively eases model learning. Last, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online. Extensive experiments and diverse applications on a large-scale fetal US dataset including 1000 volumes with 22 landmarks per volume demonstrate that our method outperforms other strong competitors.Comment: 16 pages, 11 figures, accepted by Medical Image Analysis(2023

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table

Success in books: a big data approach to bestsellers

Abstract Reading remains the preferred leisure activity for most individuals, continuing to offer a unique path to knowledge and learning. As such, books remain an important cultural product, consumed widely. Yet, while over 3 million books are published each year, very few are read widely and less than 500 make it to the New York Times bestseller lists. And once there, only a handful of authors can command the lists for more than a few weeks. Here we bring a big data approach to book success by investigating the properties and sales trajectories of bestsellers. We find that there are seasonal patterns to book sales with more books being sold during holidays, and even among bestsellers, fiction books sell more copies than nonfiction books. General fiction and biographies make the list more often than any other genre books, and the higher a book’s initial place in the rankings, the longer the book stays on the list as well. Looking at patterns characterizing authors, we find that fiction writers are more productive than nonfiction writers, commonly achieving bestseller status with multiple books. Additionally, there is no gender disparity among bestselling fiction authors but nonfiction, most bestsellers are written by male authors. Finally we find that there is a universal pattern to book sales. Using this universality we introduce a statistical model to explain the time evolution of sales. This model not only reproduces the entire sales trajectory of a book but also predicts the total number of copies it will sell in its lifetime, based on its early sales numbers. The analysis of the bestseller characteristics and the discovery of the universal nature of sales patterns with its driving forces are crucial for our understanding of the book industry, and more generally, of how we as a society interact with cultural products

Crystal structure of (E)-4-bromo-N′-(3-chloro-2-hydroxybenzylidene)benzohydrazide, C14H10BrClN2O2

C14H10BrClN2O2, orthorhombic, Pca21 (no. 29), a = 32.4137(12) Å, b = 4.6683(2) Å, c = 8.8873(3) Å, V = 1344.80(9) Å3, Z = 4, Rgt(F) = 0.0239, wRref(F2) = 0.0611, T = 150(2) K

Exploration of a deep learning-based mechanism for predicting the work competence of community caregivers

To predict the workability of community nursing staff and provide corresponding training strategies based on the results. In this study, a nursing staff workability prediction model based on R-GCN-GRU was constructed. In the process of community nursing staff workability feature extraction, the attention mechanism is introduced, combined with the degree of association between the captured nodes of the R-GCN network and the long-term memory capacity of the GRU network, and the model optimization is carried out using the cross-entropy loss function. Finally, the workability of community caregivers in a city in Guangdong Province was predicted to verify the accuracy of the model from multiple perspectives. The results showed that clinical handling ability, keen observation ability, and communication ability were more valued by most caregivers, and their selection rates all reached 98.4%. On the other hand, clinical research, organizational management, and innovation abilities were relatively low. In the ability prediction of individual characteristics, the highest income personnel’s working ability was second only to the lowest salary personnel reaching 44.61±6.03. The working ability of older age and higher-position nursing staff, and nursing staff with more than 25 years of service reached 45.62±6.14, 48.30±5.22, and 45.86±5.52, respectively

Crystal structure of 4-bromo-N′-[(3-bromo-2-hydroxyphenyl)methylidene]benzohydrazide methanol solvate, C15H14Br2N2O3

C15H14Br2N2O3, triclinic, P1‾P‾{1} (no. 2), a = 7.2748(3) Å, b = 9.0786(4) Å, c = 12.8613(5) Å, α = 99.458(2)°, β = 100.401(2)°, γ = 99.499(2)°, V = 807.35(6) Å3, Z = 2, Rgt(F) = 0.0253, wRref(F2) = 0.0673, T = 150.0 K