bluuebunny/crossref_metadata_2025_split huggingface dataset:
Occluder Generation for Buildings in Digital Games
Authors: Wu Kui , He Xu , Pan Zherong , Gao Xifeng 
Published: March 2023

AbstractOcclusion culling has become a prevalent method in modern game engines. It can significantly reduce the rendering cost by using an approximate coarse mesh (occluder) for culling hidden objects. An ideal occluder should use as few faces as possible to represent the high‐resolution input mesh with a high culling accuracy. We address the open problem of automatic occluder generation for 3D building models with complex topology and interior structures. Our method first generates two coarse sets of faces via patch‐based and voxel‐based mesh simplification techniques. A metric‐guided selection algorithm chooses the best subset of faces to form the occluder, achieving a high occlusion rate and accuracy. Over an evaluation of 77 building models, our method compares favorably against state‐of‐the‐arts in terms of occlusion accuracy, occlusion rate, and face number.
Joint Hand and Object Pose Estimation from a Single RGB Image using High‐level 2D Constraints
Authors: Song H.‐X. , Mu T.‐J. , Martin R. R. 
Published: March 2023

AbstractJoint pose estimation of human hands and objects from a single RGB image is an important topic for AR/VR, robot manipulation, etc. It is common practice to determine both poses directly from the image; some recent methods attempt to improve the initial poses using a variety of contact‐based approaches. However, few methods take the real physical constraints conveyed by the image into consideration, leading to less realistic results than the initial estimates. To overcome this problem, we make use of a set of high‐level 2D features which can be directly extracted from the image in a new pipeline which combines contact approaches and these constraints during optimization. Our pipeline achieves better results than direct regression or contact‐based optimization: they are closer to the ground truth and provide high quality contact.
EL‐GAN: Edge‐Enhanced Generative Adversarial Network for Layout‐to‐Image Generation
Authors: Gao Lin , Wu Lei , Meng Xiangxu 
Published: March 2023

AbstractAlthough some progress has been made in the layout‐to‐image generation of complex scenes with multiple objects, object‐level generation still suffers from distortion and poor recognizability. We argue that this is caused by the lack of feature encodings for edge information during image generation. In order to solve these limitations, we propose a novel edge‐enhanced Generative Adversarial Network for layout‐to‐image generation (termed EL‐GAN). The feature encodings of edge information are learned from the multi‐level features output by the generator and iteratively optimized along the generator's pipeline. Two new components are included at each generator level to enable multi‐scale learning. Specifically, one is the edge generation module (EGM), which is responsible for converting the output of the multi‐level features by the generator into images of different scales and extracting their edge maps. The other is the edge fusion module (EFM), which integrates the feature encodings refined from the edge maps into the subsequent image generation process by modulating the parameters in the normalization layers. Meanwhile, the discriminator is fed with frequency‐sensitive image features, which greatly enhances the generation quality of the image's high‐frequency edge contours and low‐frequency regions. Extensive experiments show that EL‐GAN outperforms the state‐of‐the‐art methods on the COCO‐Stuff and Visual Genome datasets. Our source code is available at https://github.com/Azure616/EL-GAN.
Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology‐Varying Objects
Authors: Wang Ziyu , Deng Yu , Yang Jiaolong , Yu Jingyi , Tong Xin 
Published: March 2023

Abstract3D‐aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images even for topology‐varying object categories. However, these methods still lack the capability to separately control the shape and appearance of the objects in the generated radiance fields. In this paper, we propose a generative model for synthesizing radiance fields of topology‐varying objects with disentangled shape and appearance variations. Our method generates deformable radiance fields, which builds the dense correspondence between the density fields of the objects and encodes their appearances in a shared template field. Our disentanglement is achieved in an unsupervised manner without introducing extra labels to previous 3D‐aware GAN training. We also develop an effective image inversion scheme for reconstructing the radiance field of an object in a real monocular image and manipulating its shape and appearance. Experiments show that our method can successfully learn the generative model from unstructured monocular images and well disentangle the shape and appearance for objects (e.g., chairs) with large topological variance. The model trained on synthetic data can faithfully reconstruct the real object in a given single image and achieve high‐quality texture and shape editing results.
Depth‐Aware Shadow Removal
Authors: Fu Yanping , Gai Zhenyu , Zhao Haifeng , Zhang Shaojie , Shan Ying , Wu Yang , Tang Jin 
Published: March 2023

AbstractShadow removal from a single image is an ill‐posed problem because shadow generation is affected by the complex interactions of geometry, albedo, and illumination. Most recent deep learning‐based methods try to directly estimate the mapping between the non‐shadow and shadow image pairs to predict the shadow‐free image. However, they are not very effective for shadow images with complex shadows or messy backgrounds. In this paper, we propose a novel end‐to‐end depth‐aware shadow removal method without using depth images, which estimates depth information from RGB images and leverages the depth feature as guidance to enhance shadow removal and refinement. The proposed framework consists of three components, including depth prediction, shadow removal, and boundary refinement. First, the depth prediction module is used to predict the corresponding depth map of the input shadow image. Then, we propose a new generative adversarial network (GAN) method integrated with depth information to remove shadows in the RGB image. Finally, we propose an effective boundary refinement framework to alleviate the artifact around boundaries after shadow removal by depth cues. We conduct experiments on several public datasets and real‐world shadow images. The experimental results demonstrate the efficiency of the proposed method and superior performance against state‐of‐the‐art methods.
Color‐mapped noise vector fields for generating procedural micro‐patterns
Authors: Grenier C. , Sauvage B. , Dischler J.‐M. , Thery S. 
Published: March 2023

AbstractStochastic micro‐patterns successfully enhance the realism of virtual scenes. Procedural models using noise combined with transfer functions are extremely efficient. However, most patterns produced today employ 1D transfer functions, which assign color, transparency, or other material attributes, based solely on the single scalar quantity of noise. Multi‐dimensional transfer functions have received widespread attention in other fields, such as scientific volume rendering. But their potential has not yet been well explored for modeling micro‐patterns in the field of procedural texturing. We propose a new procedural model for stochastic patterns, defined as the composition of a bi‐dimensional transfer function (a.k.a. color‐map) with a stochastic vector field. Our model is versatile, as it encompasses several existing procedural noises, including Gaussian noise and phasor noise. It also generates a much larger gamut of patterns, including locally structured patterns which are notoriously difficult to reproduce. We leverage the Gaussian assumption and a tiling and blending algorithm to provide real‐time generation and filtering. A key contribution is a real‐time approximation of the second order statistics over an arbitrary pixel footprint, which enables, in addition, the filtering of procedural normal maps. We exhibit a wide variety of results, including Gaussian patterns, profiled waves, concentric and non‐concentric patterns.
Pixel Art Adaptation for Handicraft Fabrication
Authors: Igarashi Yuki , Igarashi Takeo 
Published: March 2023

AbstractKnitting and weaving patterns can be visually represented as pixel art. With hand knitting and weaving, human error (shifting, duplicating, or skipping pixels) can occur during manual fabrication. It is too costly to change already‐fabricated pixels, so experts often adapt pixels that have not yet been fabricated to make the errors less visible. This paper proposes an automatic adaptation process to minimize visual artifacts. The system presents multiple adaptation possibilities to the user, who can choose the proposed adaptation or untie and re‐fabricate their work. In typical handicraft fabrication, the design is complete before the start of fabrication and remains fixed during fabrication. Our system keeps updating the design during fabrication to tolerate human errors in the process. We implemented the proposed algorithm in a system that visualizes the knitting pattern, cross‐stitching and bead weaving processes.
Efficient Texture Parameterization Driven by Perceptual‐Loss‐on‐Screen
Authors: Sun Haoran , Wang Shiyi , Wu Wenhai , Jin Yao , Bao Hujun , Huang Jin 
Published: March 2023

AbstractTexture mapping is a ubiquitous technique to enrich the visual effect of a mesh, which represents the desired signal (e.g. diffuse color) on the mesh to a texture image discretized by pixels through a bijective parameterization. To achieve high visual quality, large number of pixels are generally required, which brings big burden in storage, memory and transmission. We propose to use a perceptual model and a rendering procedure to measure the loss coming from the discretization, then optimize a parameterization to improve the efficiency, i.e. using fewer pixels under a comparable perceptual loss. The general perceptual model and rendering procedure can be very complicated, and non‐isotropic property rooted in the square shape of pixels make the problem more difficult to solve. We adopt a two‐stage strategy and use the Bayesian optimization in the triangle‐wise stage. With our carefully designed weighting scheme, the mesh‐wise optimization can take the triangle‐wise perceptual loss into consideration under a global conforming requirement. Comparing with many parameterizations manually designed, driven by interpolation error, or driven by isotropic energy, ours can use significantly fewer pixels with comparable perception loss or vise vesa.
Learning Dynamic 3D Geometry and Texture for Video Face Swapping
Authors: Otto C. , Naruniec J. , Helminger L. , Etterlin T. , Mignone G. , Chandran P. , Zoss G. , Schroers C. , Gross M. , Gotardo P. , Bradley D. , Weber R. 
Published: March 2023

AbstractFace swapping is the process of applying a source actor's appearance to a target actor's performance in a video. This is a challenging visual effect that has seen increasing demand in film and television production. Recent work has shown that data‐driven methods based on deep learning can produce compelling effects at production quality in a fraction of the time required for a traditional 3D pipeline. However, the dominant approach operates only on 2D imagery without reference to the underlying facial geometry or texture, resulting in poor generalization under novel viewpoints and little artistic control. Methods that do incorporate geometry rely on pre‐learned facial priors that do not adapt well to particular geometric features of the source and target faces. We approach the problem of face swapping from the perspective of learning simultaneous convolutional facial autoencoders for the source and target identities, using a shared encoder network with identity‐specific decoders. The key novelty in our approach is that each decoder first lifts the latent code into a 3D representation, comprising a dynamic face texture and a deformable 3D face shape, before projecting this 3D face back onto the input image using a differentiable renderer. The coupled autoencoders are trained only on videos of the source and target identities, without requiring 3D supervision. By leveraging the learned 3D geometry and texture, our method achieves face swapping with higher quality than when using off‐the‐shelf monocular 3D face reconstruction, and overall lower FID score than state‐of‐the‐art 2D methods. Furthermore, our 3D representation allows for efficient artistic control over the result, which can be hard to achieve with existing 2D approaches.
BareSkinNet: De‐makeup and De‐lighting via 3D Face Reconstruction
Authors: Yang Xingchao , Taketomi Takafumi 
Published: March 2023

AbstractWe propose BareSkinNet, a novel method that simultaneously removes makeup and lighting influences from the face image. Our method leverages a 3D morphable model and does not require a reference clean face image or a specified light condition. By combining the process of 3D face reconstruction, we can easily obtain 3D geometry and coarse 3D textures. Using this information, we can infer normalized 3D face texture maps (diffuse, normal, roughness, and specular) by an image‐translation network. Consequently, reconstructed 3D face textures without undesirable information will significantly benefit subsequent processes, such as re‐lighting or re‐makeup. In experiments, we show that BareSkinNet outperforms state‐of‐the‐art makeup removal methods. In addition, our method is remarkably helpful in removing makeup to generate consistent high‐fidelity texture maps, which makes it extendable to many realistic face generation applications. It can also automatically build graphic assets of face makeup images before and after with corresponding 3D data. This will assist artists in accelerating their work, such as 3D makeup avatar creation.
Ivacaftor: Five‐year outcomes in the West of Scotland cystic fibrosis population
Authors: Al‐Din Yasmin , Dryden Carol , MacGregor Gordon , Young David , Coelho Cristina 
Published: March 2023

AbstractIntroductionIvacaftor has shown to be effective in patients with cystic fibrosis (CF) with a G551D mutation.ObjectivesThis work aims to evaluate ivacaftor's effectiveness and safety in the real world, over 5 years, in the West of Scotland CF population.MethodsWe evaluated ivacaftor's effect on pulmonary function, body mass index (BMI), hospital bed occupancy, and adverse effects in patients ≥6 years with at least one G551D mutation.ResultsStatistically significant increases from baseline were observed in mean per cent predicted forced expiratory volume in 1 s (FEV1) at year 1 (which was maintained at years 2 and 5) and BMI over 5 years in our adolescent/adult cohort. Improvements were observed in per cent predicted FEV1 within the paediatric cohort with a suggestion of a plateau effect. The increase in paediatric BMI z‐score was nonstatistically significant. There was a reduction in the number of pulmonary exacerbations requiring intravenous antibiotics and hospital bed occupancy. Ivacaftor was well tolerated.ConclusionIvacaftor was effective in our population.
The impact of pre‐existing hematologic disorders on morbidity and mortality following heart transplantation: Focus on early graft dysfunction
Authors: Carey Matthew R. , Ladanyi Annamaria , Mehlman Yonatan , Molinsky Rebecca L. , Eisenberger Andrew , Clerkin Kevin J. , Aaron Justin G. , Takeda Koji , Sayer Gabriel T. , Uriel Nir , Demmer Ryan T. , Colombo Paolo C. , Yuzefpolskaya Melana 
Published: March 2023

AbstractBackgroundHeart transplantation (HT) is the gold standard therapy for advanced heart failure, providing excellent long‐term outcomes. However, postoperative outcomes are limited by bleeding, infections, and primary graft dysfunction (PGD) that contribute to early mortality after HT. HT candidates with pre‐existing hematologic disorders, bleeding, and clotting, may represent a higher risk population. We assessed the short‐ and long‐term outcomes of patients with pre‐existing hematologic disorders undergoing HT.Methods and resultsMedical records of all adult patients who received HT from January 2010 to December 2019 at our institution were retrospectively reviewed. Hematologic disorders were identified via chart review and adjudicated by a board‐certified hematologist. Inverse probability weighting and multivariable models were used to adjust for potential pretransplant confounders. Four hundred and ninety HT recipients were included, of whom 29 (5.9%) had a hematologic disorder. Hematologic disorders were associated with severe PGD requiring mechanical circulatory support (aOR 3.15 [1.01–9.86]; p = .049), postoperative infections (aOR 2.93 [1.38–6.23]; p = .01), and 3‐year acute cellular rejection (ACR) (≥1R/1B) (aSHR 2.06 [1.09–3.87]; p = .03). There was no difference in in‐hospital mortality (aOR 1.23 [.20–7.58], p = .82) or 3‐year mortality (aHR 1.58 [.49–5.12], p = .44).ConclusionsPatients with hematologic disorders undergoing HT are at increased risk of severe PGD, postoperative infections, and ACR, while in‐hospital and 3‐year mortality remain unaffected.
Multi‐attribute group decision‐making based on linguistic Pythagorean fuzzy copula extended power average operator
Authors: Kou Yaqing , Wang Jun , Xu Wuhuan , Xu Yuan 
Published: March 2023

AbstractThis paper aims to propose a novel multi‐attribute group decision‐making (MAGDM) method based on linguistic Pythagorean fuzzy copula extended power average operator. Existing researches under linguistic Pythagorean fuzzy environment lack of the ability to handle with extreme values and the flexible operational rules. To fill these two gaps, this paper first provides the definition of Archimedean copula and co‐copula operational rules under linguistic Pythagorean fuzzy environment, which can reflect the connection among arguments and provide more choices for experts to express their preferences. Then, we gather the extended power average (EPA) operator to present some new aggregation operators, which can reduce the negative influence of extreme evaluation values. To show the application of the proposed method to MAGDM problems, we apply it to handle a case of takeout O2O platform assessment problem. The numerical case and comparative analysis with other existing methods illustrate that our proposed method is more scientific and flexible.
Nanoplastics modulate the outcome of a zooplankton–microparasite interaction
Authors: Mavrianos Stylianos , Manzi Florent , Agha Ramsy , Azoubib Noemi , Schampera Charlotte , Wolinska Justyna 
Published: March 2023

Abstract

The accumulation and degradation of plastic waste in freshwater bodies poses a threat to aquatic biota. Microplastics (<5 mm) can transfer upwards in food chains and have been shown to induce deleterious effects on important players of freshwater ecosystems, including zooplankton.

A smaller category of microplastic particles, the so‐called nanoplastics (≤100 nm) raise special concern due to their ability to act at sub‐cellular and molecular levels. Despite growing knowledge of their effects on physiological traits of individual species, the way they affect interactions between species remains largely unexplored.

We studied the effects of nanoplastics on host–parasite interactions by exposing the zooplankton host Daphnia galeata × longispina to the parasitic yeast Metschnikowia bicuspidata without plastic and at two different concentrations of polystyrene nanoplastic beads (100 nm): 5 and 20 mg/L.

Both concentrations of nanoplastics increased the proportion of infected hosts; at the higher concentration, however, elevated rates of host mortality and impaired spore production cancelled out the parasite's advantage. Consequently, parasite success was greatest at the lower level of nanoplastic exposure.

Infection by Metschnikowia greatly reduced host lifespan and total offspring production (regardless of nanoplastic exposure), but only decreased the proportion of successfully reproducing hosts when Daphnia were additionally exposed to nanoplastics. Nanoplastics alone did not cause such a reduction in host fitness parameters: instead, the lower concentration increased lifetime offspring production by about 50%, suggesting hormesis.

Given that parasitism is a ubiquitous lifestyle in nature and that parasites can play important roles in the shaping and functioning of ecosystems, these results highlight the importance of including interactions between host and parasite species as alternative ecotoxicological endpoints to better assess the ecological consequences of plastic pollution.
A shift in paradigm? Collaborative public administration in the context of national digitalization strategies
Authors: Hammerschmid Gerhard , Palaric Enora , Rackwitz Maike , Wegrich Kai 
Published: March 2023

AbstractDespite claims of a paradigmatic shift toward the increased role of networks and partnerships as a form of governance—driven and enabled by digital technologies—the relation of “Networked Governance” with the pre‐existing paradigms of “Traditional Weberian Public Administration” and “New Public Management” remains relatively unexplored. This research aims at collecting systematic evidence on the dominant paradigms in digitalization reforms in Europe by comparing the doctrines employed in the initial and most recent digitalization strategies across eight European countries: Estonia, France, Germany, Italy, The Netherlands, Norway, Spain, and the United Kingdom. We challenge the claim that Networked Governance is emerging as the dominant paradigm in the context of the digitalization of the public sector. The findings confirm earlier studies indicating that information and communication technologies tend to reinforce some traditional features of administration and the recentralization of power. Furthermore, we find evidence of the continued importance of key features of “New Public Management” in the digital era.