TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The highlight is that, the mining scheme of APM can be widely adapted to different frameworks and bring consistent benefits. Specifically, we first present a novel Action-related Text Prompt module (ATP) that directly embeds action labels and transfers the rich language information in the label to the pose sequence. Besides, we further introduce Action-specific Pose Prompt module (APP) to mine the position-aware pose pattern of each action, and exploit the correlation between the mined patterns and input pose sequence for further pose refinement. Experiments show that APM can improve the performance of most video-based 2D-to-3D HPE frameworks by a large margin.Comment: 6 pages, 4 figures, 2023ICM

2.5D cascaded context-based network for liver and tumor segmentation from CT images

The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. The pose-oriented self-attention mechanism explicitly models the topological interactions between body joints, whereas the distance-related position embedding encodes the distance of joints to the root joint to distinguish groups of joints with different difficulties in regression. Furthermore, we present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints, by considering the estimated uncertainty of each joint with uncertainty-guided sampling strategy and self-attention mechanism. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods with reduced model parameters on 3D HPE benchmarks such as Human3.6M and MPI-INF-3DHPComment: accepted by AAAI202

High expression of ubiquitin-conjugating enzyme 2C (UBE2C) correlates with nasopharyngeal carcinoma progression

BACKGROUND: Overexpression of ubiquitin-conjugating enzyme 2C (UBE2C) has been detected in many types of human cancers, and is correlated with tumor malignancy. However, the role of UBE2C in human nasopharyngeal carcinoma (NPC) is unclear. In this study, we investigated the role of aberrant UBE2C expression in the progression of human NPC. METHODS: Immunohistochemical analysis was performed to detect UBE2C protein in clinical samples of NPC and benign nasopharyngeal tissues, and the association of UBE2C expression with patient clinicopathological characteristics was analyzed. UBEC2 expression profiles were evaluated in cell lines representing varying differentiated stages of NPC and immortalized nasopharyngeal epithelia NP-69 cells using quantitative RT-PCR, western blotting and fluorescent staining. Furthermore, UBE2C was knocked down using RNA interference in these cell lines and proliferation and cell cycle distribution was investigated. RESULTS: Immunohistochemical analysis revealed that UBE2C protein expression levels were higher in NPC tissues than in benign nasopharyngeal tissues (P<0.001). Moreover, high UBE2C protein expression was positively correlated with tumor size (P=0.017), lymph node metastasis (P=0.016) and distant metastasis (P=0.015) in NPC patients. In vitro experiments demonstrated that UBE2C expression levels were inversely correlated with the degree of differentiation of NPC cell lines, whereas UBE2C displayed low level of expression in NP-69 cells. Knockdown of UBE2C led to significant arrest at the S and G2/M phases of the cell cycle, and decreased cell proliferation was observed in poorly-differentiated CNE2Z NPC cells and undifferentiated C666-1 cells, but not in well-differentiated CNE1 and immortalized NP-69 cells. CONCLUSIONS: Our findings suggest that high expression of UBE2C in human NPC is closely related to tumor malignancy, and may be a potential marker for NPC progression

Long-term whole blood DNA preservation by cost-efficient cryosilicification

This work was supported by the National Natural Science Foundation of China (21972047 to W.Z., 52003086 to Q.L.), Guangdong Provincial Pearl River Talents Program (2019QN01Y314 to Q.L.), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08Y318 to W.Z.), Natural Science Foundation of Guangdong Province, China (2021A1515010724 to Q.L.), China Postdoctoral Science Foundation (2020M672625, 2021T140213 to Q.L.), Science and Technology Project of Guangzhou, China (202102020352 to W.Z., 202102020259 to Q.L.), the Fundamental Research Funds for the Central Universities of China. The authors thank the support from the Guangzhou Women and Children’s Medical Center and Laboratory Animal Research Center of the South China University of Technology. S.W. acknowledges funding from the Basque Government Industry Department under the ELKARTEK and HAZITEK programs.Deoxyribonucleic acid (DNA) is the blueprint of life, and cost-effective methods for its long-term storage could have many potential benefits to society. Here we present the method of in situ cryosilicification of whole blood cells, which allows long-term preservation of DNA. Importantly, our straightforward approach is inexpensive, reliable, and yields cryosilicified samples that fulfill the essential criteria for safe, long-term DNA preservation, namely robustness against external stressors, such as radical oxygen species or ultraviolet radiation, and long-term stability in humid conditions at elevated temperatures. Our approach could enable the room temperature storage of genomic information in book-size format for more than one thousand years (thermally equivalent), costing only 0.5 $/person. Additionally, our demonstration of 3D-printed DNA banking artefacts, could potentially allow 'artificial fossilization'.Publisher PDFPeer reviewe

Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning

Keloid is a pathological scar formed by abnormal wound healing, characterized by the persistence of local inflammation and excessive collagen deposition, where the intensity of inflammation is positively correlated with the size of the scar formation. The pathophysiological mechanisms underlying keloid formation are unclear, and keloid remains a therapeutic challenge in clinical practice. This study is the first to investigate the role of glycosphingolipid (GSL) metabolism pathway in the development of keloid. Single cell sequencing and microarray data were applied to systematically analyze and screen the glycosphingolipid metabolism related genes using differential gene analysis and machine learning algorithms (random forest and support vector machine), and a set of genes, including ARSA,GBA2,SUMF2,GLTP,GALC and HEXB, were finally identified, for which keloid diagnostic model was constructed and immune infiltration profiles were analyzed, demonstrating that this set of genes could serve as a new therapeutic target for keloid. Further unsupervised clustering was performed by using expression profiles of glycosphingolipid metabolism genes to discover keloid subgroups, immune cells, inflammatory factor differences and the main pathways of enrichment between different subgroups were calculated. The single-cell resolution transcriptome landscape concentrated on fibroblasts. By calculating the activity of the GSL metabolism pathway for each fibroblast, we investigated the activity changes of GSL metabolism pathway in fibroblasts using pseudotime trajectory analysis and found that the increased activity of the GSL metabolism pathway was associated with fibroblast differentiation. Subsequent analysis of the cellular communication network revealed the existence of a fibroblast-centered communication regulatory network in keloids and that the activity of the GSL metabolism pathway in fibroblasts has an impact on cellular communication. This contributes to the further understanding of the pathogenesis of keloids. Overall, we provide new insights into the pathophysiological mechanisms of keloids, and our results may provide new ideas for the diagnosis and treatment of keloids

A Parametric Study of Coupled Hydromechanical Behaviors Induced by Shallow Water Flow in Shallow Sediments in Deepwater Drilling Based on Numerical Modeling

Shallow water flow is a geohazard encountered in deepwater drilling. It is often characterized by excessive water flow into the wellbore caused by the pressure difference between overpressured sediments and the wellbore, and it usually leads to serious well control problems and may eventually result in the loss of a well. Many research efforts focused on the identification of shallow water flow zones and the associated water flow in the drilled wellbore. Not many studies investigated the coupled hydromechanical behaviors in sediments during the occurrence of shallow water flow, while such behaviors are directly related to uncontrolled flow in the wellbore and solid deformation. Based on a coupled hydraulic-mechanical model and finite element methods, this work investigates the temporal-spatial evolutions of near-well pressure and stress induced by shallow water flow. Hydraulic behaviors in the deepwater shallow sediments are described by saturated fluid flow in porous media while mechanical behaviors in the sediments are depicted by linear elasticity. Finite element methods are used for the numerical solution to the coupled hydraulic-mechanical formulation. The study then conducts a series of parametric studies to quantitatively understand the effects of relevant parameters on pressure, stress, and uncontrolled flow into the wellbore. Results indicate that overpressure has the most significant impact while Young’s modulus has the most limited impact on spatial-temporal pressure/stress evolutions and the uncontrolled water production in the wellbore. Permeability, porosity, water viscosity, and water compressibility all have certain effects on near-well physical characteristics and wellbore water production. In addition, it is noted that pressure drainage and induced stress are more significant when it is closer to the wellbore. This numerical study helps to quantitatively identify the most influential parameters related to shallow water flow and calculates the water mass flow loaded in the wellbore