bluuebunny/crossref_metadata_2025 huggingface dataset:
Onlies and Firsts: My Path in Geoscience
Authors: Williams-Stroud Sherilyn
Published: July 2024

Underrepresentation is a significant issue in the geoscience profession, particularly in academia. At the time I was pursuing my Ph.D. degree in the geosciences, I was one of only a handful of black women doing so. I will recount events in my life that led me to geoscience, but also those that helped me want to continue to work in this field. The fact that my professional journey contains so many situations where I was a “first” and/or an “only” seemed to me to be less an accomplishment than simply a condition that needed to be changed. If not me, it would have been somebody else. I have surmised through discussions with other women of color in geoscience over the course of my career that seeing my path as merely a condition to navigate may have significantly contributed to my ability to overcome obstacles. There were things I wanted to do, and I was determined to figure out a way to do them. I share my life story because I did not take a straight line to geology, or even a straight line through school. I had to juggle family and career in the way that many women do, I met and got to know many unique and interesting people, and I had the opportunity to travel to many parts of the world. I will name names to acknowledge my significant mentors. It is because of many positive experiences and support from them that I have such a difficult time coming up with negative experiences. I hope my unique path may help readers to develop a deeper understanding of some of the subtle obstacles that have been keeping the numbers of black women in geoscience so low for decades, illuminate the ways that those obstacles can be traversed, and contribute to a little bit of change for the future.
On the delayed expression of mantle inheritance–controlled strain localization during rifting
Authors: Zwaan Frank , Erratt Duncan , Manatschal Gianreto , Chenin Pauline , Schreurs Guido
Published: July 2024

In this paper, we explore the relative impact mantle inheritance can have on the evolution of magma-poor rift systems during the early stages of rift localization. To this end, we revisit the tectonic history of the Mesozoic Northeast Atlantic and its North Sea subdomain, analyzing these observations through comparison with results from recent analogue tectonic modeling work. Our analysis suggests that initial broadly distributed rift basin formation may be controlled by crustal inheritance, whereas the subsequent localization of deformation along deeper mantle inheritance may have caused the overprinting of previous rift basin trends in the Northeast Atlantic. This overprinting became possible as soon as the thinning of the ductile crust during progressive rifting allowed for sufficient coupling between the mantle and the upper crust. Importantly, we suggest that no changes in plate motion direction due to large-scale reorganization of the plate tectonic system are needed for differently oriented basin trends overprinting each other to develop. With these insights, we propose an updated scenario for rift kinematics in the North Sea involving continuous E-W plate divergence and provide a framework to rethink the evolution of other rift systems around the world during their early stages of rift localization.
Supplemental Material: Cretaceous magnetostratigraphy of the southern Simao Basin, SE Tibetan Plateau, and its paleogeographic implications
Authors: Zhang Dawen , Yan maodu , al. et
Published: July 2024

Tables S1–S3 and Figures S1–S8.
Reassessing reverse triage in future conflict
Authors: Hurst Daniel J , Ray T L
Published: July 2024

Future warfare will likely involve near-peer or peer-peer conflict in which there is a great risk of mass casualty scenarios. Because of anti-access and area denial, air superiority will not be guaranteed, which will hamper rapid evacuation of casualties as well as resupply. Under such circumstances, military medical personnel may be forced, due to the constraints of the battlefield and tactical necessity to return servicemembers to duty, to implement reverse triage in which servicemembers with less severe injuries are treated first. However, reverse triage is potentially incongruent with international humanitarian law. Furthermore, should reverse triage need to be implemented, from the extant military doctrine it is not certain when this would be appropriate or the steps that might be followed, which highlight the gaps that exist before reverse triage should be considered as military doctrine on the battlefield. Lastly, we question the psychological impact that reverse triage could portend on military medical personnel, unit morale and unit cohesion. While there have been recent recommendations that reverse triage might need to be implemented in a near-term future conflict, these issues linger. It is time for Western militaries to assess the merits of reverse triage and the potential drawbacks.
More variables or more bins? Impact on the EFT interpretation of Drell–Yan measurements
Authors: Grossi Samuele , Torre Riccardo
Published: July 2024

We generalize previous studies on constraining operators of the Standard Model Effective Field Theory using Drell-Yan (DY) measurements to include at the same time all relevant operators and uncertainties. It has been shown that fully differential measurements (triple differential for neutral and double differential for charged) are more sensitive to EFT effects. Nevertheless, due to the finite statistics, the fully differential measurements sacrifice some statistical power on the shape (less invariant mass or transverse momentum bins) in favour of more kinematic variables. We show that when the observables are particularly sensitive to the shape of the distributions, such as the invariant mass of the two leptons in neutral DY, the single differential measurement with more bins, may be as sensitive as the fully differential one, at least for specific EFT operators. This suggests to always supplement fully differential analyses with projections into the relevant distributions evaluated with finer bins.
Quantum Software Engineering: Practical Challenges
Authors: Piattini Mario , Hevia José Luis , Peterssen Guido
Published: July 2024

Quantum computing is a young discipline that specializes in the construction of hardware and software using the quantum properties of nature, to solve problems of higher complexity that classical computing cannot address, in multiple business areas. The most important computing revolution of the last 60 years has begun because of the integration of classical computing, quantum computing and artificial intelligence. In this paper, we present a pragmatic survey of the main quantum computing areas, focusing in particular on software and the need for real Quantum Software Engineering (QSE) to produce quantum software with sufficient quality and productivity, which is the fundamental axis for the diffusion of quantum computing.
On the Applications of a New Advanced PEPFG Mathematical Model for HTM Dynamic Behavior of SDF Sandwich Plates
Authors: Kargar Javad , Rahimi Shirin Mohammad , Elhadi Khaled Mohamed , El Ouni Mohamed Hechmi , Ghazouani Nejib
Published: July 2024

Considering the crucial role of the flexoelectricity phenomena (the electrical polarization–strain gradient coupling) in the mechanical behavior of different structures on the micro/nanoscale, this paper is derives the motion equations of a size-dependent flexoelectric (SDF) sandwich plate to study its hygro-thermo-mechanical (HTM) dynamic behavior. The model is a three-layer microplate including a porous exponential and power–law functionally graded (PEPFG) mid-layer, and two flexoelectric face sheets. The whole structure is under hygrothermal loading and the Vlasov foundation serves as the stand for the structure. Then, Hamilton’s principle and modified couple stress theory (MCST) are implemented to derive the motion equations. The new PEPFG utilization besides evaluating the impact of the length scale parameter, hygrothermal loading, materials composition, porosity, foundation parameter, and boundary conditions on the normalized natural frequency (NNF) of such an SDF sandwich model is the novelty of this paper. It is revealed that the lateral ratio enhancement causes NNF and stiffness reduction in the model. Furthermore, among all considered boundary types, simply supported and clamped refer to the lowest and the highest NNFs, respectively. This paper serves as a good source to provide a better understanding of the dynamic response of high stiffness-to-weight ratio structures to different variable variations.
Decoupled Calculation and Case Study Analysis of Friction Pendulum Bearing Considering the Jumping-Off Effect
Authors: Wei Biao , Yang Zhixing , Fu Yunji , Chen Jun , Jiang Lizhong , Chen Mingyu , Zheng Xianglin
Published: July 2024

Under the coupled horizontal and vertical seismic actions, the Friction Pendulum Bearing (FPB) exhibits a phenomenon where the slider jumps off and detaches from the lower plate. When the jumping phenomenon occurs in the FPB, there are complex issues of motion coupling, friction coupling, and friction nonlinearity between the bearing plate and the slider. Current research overlooks the impact of the jumping-off effect on the response of the FPB, and the widely used Bouc–Wen model in existing studies is incapable of analyzing this effect. To address these problems, this paper abandons the traditional energy equivalence method and innovatively proposes a theory for analyzing the jumping-off effect by combining the multi-contact point vector projection method with the friction decoupling analysis method. This theory is applied to decouple the response analysis of the FPB under the jumping-off effect and elucidate the amplification effect caused by the jumping-off through numerical examples, paving the way for subsequent precise analysis and laying a theoretical foundation.
Complex Dispersion Analysis of Flexural Wave Propagation in Periodic Track Structure
Authors: Liu Jian , Feng Qingsong , Yang Zhou , Chen Yao
Published: July 2024

The periodic characteristics of the track structure have filtering characteristics for the propagation of vibration waves within it, and the dispersion analysis is of great significance to understand the vibration transmission characteristics of the track structure. The accuracy of the traditional virtual spring method depends on the value of spring stiffness, which leads to poor stability. Therefore, this paper adopts a null space method with both stability, accuracy and efficiency, and takes the CRTSIII ballastless track structure as the research object. Based on the null space method and energy functional variational principle, the damping factor and the material frequency variation effect are considered in detail, and the complex frequency dispersion of the CRTSIII ballastless track structure is analyzed. The accuracy of the proposed method is verified by comparing it with the results of the existing literature, and the influence of fastener stiffness frequency variation, fastener damping and concrete support layer damping on the complex dispersion characteristics of vibration waves of track structures is analyzed by using this method. The results show that the stiffness frequency effect, fastener damping and concrete support layer damping have a great influence on the attenuation domain and attenuation rate of vibration, which must be considered in the vibration transmission analysis of track structures.
Deep Learning Frequency Loss Functions for Predicting the Seismic Responses of Structures
Authors: Tang Wei-Jian , Wang Dong-Sheng , Dai Jian-Cheng , Tong Lei , Sun Zhi-Guo
Published: July 2024

Novel loss functions for seismic response prediction, such as physics-informed loss functions, have attracted considerable attention. However, existing loss functions are based on the time domain and do not consider the frequency domain differences contained in structure response signals. This study accordingly designed three loss functions based on frequency domain information — one pure frequency domain loss function and two combined frequency- and time-domain loss functions — using Fourier transforms. The proposed loss functions were applied using a trusted publicly available dataset for a frame structure, and their performances were compared with those obtained using the conventional mean squared error (MSE) loss function. The proposed frequency domain loss function exhibited superior performance, accelerating the convergence of learning curves for long-period signals while simultaneously enhancing their frequency domain accuracy and facilitating the prediction of high-sampling-rate response signals. In addition, the designed loss functions exhibited more stable performances and superior accuracy than the MSE when random dataset partitioning was employed. Finally, the combined time- and frequency-domain loss functions were shown to predict the seismic displacement time-history of tall, long-span bridges more accurately than the MSE by training the model to predict structural acceleration responses, then integrating them to predict displacement responses.
Electrochemical DNA Biosensors for the Detection of TP53 Gene Mutation Based on MWCNT and Gold Nanoparticles Embedded in Polypyrrole
Authors: Mutashar Dhulfiqar S. , Saud Ghadah Safaa , Jabbar Ahmed Q. , Kadhim Ruqaya Fouad , Khalef Wafaa Khalid , Saleh Wasan R.
Published: July 2024

A novel platform has been developed for the sensitive label-free detection of mutation in the TP53 gene, one of the most prominent genes in cancer research. The platform is based on the electrochemical properties of functionalized multi-walled carbon nanotubes (f-MWCNTs), gold nanoparticles (AuNPs), and polypyrrole (PPy). The microscopic structure and morphology of the prepared films on an ITO electrode surface were characterized using X-ray diffractometry (XRD), field emission scanning electron microscopy (FE-SEM), and Fourier transform infrared (FTIR). The hybridization events specific to the TP53 gene were monitored using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The modified electrode, which incorporates vertically aligned f-MWCNTs arrays and AuNPs, exhibited enhanced probe DNA attachment density, improving the sensitivity of the DNA sensor. The combination of AuNPs/PPy with an in-situ produced f-MWCNTs array provides a viable strategy for developing a highly sensitive biosensor for fast mutation detection in human cancer types.
Productivity Measurement with the Use of Pose Classification and Machine Learning with Fuzzy Approximate Reasoning
Authors: Ng C. Y. , Lee W. H.
Published: July 2024

Object detection refers to investigating the relationships between images or videos and detected objects to improve system utilization and make better decisions. Productivity measurement plays a key role in assessing operational efficiency across different industries. However, capturing the workers’ working status can be resource-intensive and constrained by a limited sample size if the sampling is conducted manually. While the use of object detection approaches has provided a shortcut for collecting image samples, classifying human poses involves training pose estimation models that may often require a substantial effort for annotating the images. In this study, a systematic approach that integrates pose estimation techniques, fuzzy-set theory, and machine learning algorithms has been proposed at an affordable level of computational resources. The Random Forests algorithm has been explored for handling classification tasks, while fuzzy approximation has also been applied to capture the imprecision associated with human poses, enhancing robustness to variability and accounting for inherent uncertainty. Decision-makers can utilize the proposed approach without the need for high computational resources or extensive data collection efforts, making it suitable for deployment in various workplace environments.
The teleparallel Dirac–Born–Infeld scalar approach
Authors: Amiri Maryam , Akbarieh Amin Rezaei , Kucukakca Yusuf , Haghighat Mansour
Published: July 2024

In this study, we investigate the behavior of the Dirac–Born–Infeld (DBI) scalar field as a candidate for the nature of dark energy in a non-minimal coupling scenario with the scalar torsion within the framework of teleparallel gravity. We present numerical solutions for the equations of motion and analyze the behavior of various cosmological parameters for different values of the parameter [Formula: see text] and [Formula: see text]. Our findings reveal that the DBI scalar field can contribute significantly to the energy density of the universe and drive the late-time acceleration of the universe, with a transition from deceleration to acceleration occurring within a redshift range consistent with recent observational bounds. We show that the present-day value of the deceleration parameter is consistent with recent observational constraints, and the DBI scalar field behaves like a cosmological constant model. Our results suggest that the DBI scalar field can play a significant role in driving the late-time acceleration of the universe and that it has the potential to be a viable alternative to the cosmological constant model. Further investigations are necessary to better understand the nature of the dark energy component and its implications for cosmology within the framework of teleparallel gravity.
Exploring stream patterns of titania-CNTs blended blood in a squeezed artery subject to electro-magnetization, Hall and ion-slip currents
Authors: Das Subhendu , Das Sanatan
Published: July 2024

Exploring electrokinetics phenomena in the context of diverse currents and nanoparticle blends introduces the possibility of novel biomedical approaches. This research is aimed at exploring the streaming patterns of electro-magnetized blood infused with titanium dioxide nanoparticles and single-walled carbon nanotubes (SWCNTs) in a squeezed arterial channel under the influence of electroosmosis. The model is deliberated through the imposition of an intense external magnetic field that is aligned perpendicular to the channel, resulting in effects such as Hall and ion-slip currents and Joule warming. The distribution of electric potential within the electric double layer (EDL) is predicted by solving the Poisson–Boltzmann equation. The Runge–Kutta–Fehlberg (RKF45) shooting scheme via bvp4c solver in Mathematica is hired to execute the proposed blood flow model computation. By presenting informative graphs and tables, a study is carried out to comprehend and elucidate the physical effects of crucial factors on flow dynamical features and physical quantities. A statistical analysis is carried out to identify the intricate association of model parameters with physical flow characteristics, enriching the study’s novelty. Some key outcomes are that the blood temperature attenuates with swilling values of Hall and ion slip parameters. The skin friction on the artery’s upper wall is lessened when the electroosmosis parameter gets increase. Expanding volume fraction of nanoparticles results in an elevation of the Nusselt number on the upper wall. In squeezed artery, the streamlines in the blood flow are notably aligned. The study’s findings could contribute to improving drug delivery strategies, diagnostics, medical imaging, patient care, treatment outcomes, design of biomedical devices, such as stents and catheters, and understanding of blood flow behavior in intricate arterial configurations.
Mesh Neural Cellular Automata
Authors: Pajouheshgar Ehsan , Xu Yitao , Mordvintsev Alexander , Niklasson Eyvind , Zhang Tong , Süsstrunk Sabine
Published: July 2024

Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align more closely with the ways textures form in nature. We propose Mesh Neural Cellular Automata (MeshNCA), a method that directly synthesizes dynamic textures on 3D meshes without requiring any UV maps. MeshNCA is a generalized type of cellular automata that can operate on a set of cells arranged on non-grid structures such as the vertices of a 3D mesh. MeshNCA accommodates multi-modal supervision and can be trained using different targets such as images, text prompts, and motion vector fields. Only trained on an Icosphere mesh, MeshNCA shows remarkable test-time generalization and can synthesize textures on unseen meshes in real time. We conduct qualitative and quantitative comparisons to demonstrate that MeshNCA outperforms other 3D texture synthesis methods in terms of generalization and producing high-quality textures. Moreover, we introduce a way of grafting trained MeshNCA instances, enabling interpolation between textures. MeshNCA allows several user interactions including texture density/orientation controls, grafting/regenerate brushes, and motion speed/direction controls. Finally, we implement the forward pass of our MeshNCA model using the WebGL shading language and showcase our trained models in an online interactive demo, which is accessible on personal computers and smartphones and is available at https://meshnca.github.io/.
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations
Authors: Yao Heyuan , Song Zhenhua , Zhou Yuyang , Ao Tenglong , Chen Baoquan , Liu Libin
Published: July 2024

In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based reinforcement learning, our approach effectively learns motion embeddings from a large, unstructured dataset spanning tens of hours of motion examples. The resultant motion representation not only captures diverse motion skills but also offers a robust and intuitive interface for various applications. We demonstrate the versatility of MoConVQ through several applications: universal tracking control from various motion sources, interactive character control with latent motion representations using supervised learning, physics-based motion generation from natural language descriptions using the GPT framework, and, most interestingly, seamless integration with large language models (LLMs) with in-context learning to tackle complex and abstract tasks.
Cybersickness Reduction via Gaze-Contingent Image Deformation
Authors: Groth Colin , Magnor Marcus , Grogorick Steve , Eisemann Martin , Didyk Piotr
Published: July 2024

Virtual reality has ushered in a revolutionary era of immersive content perception. However, a persistent challenge in dynamic environments is the occurrence of cybersickness arising from a conflict between visual and vestibular cues. Prior techniques have demonstrated that limiting illusory self-motion, so-called vection, by blurring the peripheral part of images, introducing tunnel vision, or altering the camera path can effectively reduce the problem. Unfortunately, these methods often alter the user's experience with visible changes to the content. In this paper, we propose a new technique for reducing vection and combating cybersickness by subtly lowering the screen-space speed of objects in the user's peripheral vision. The method is motivated by our hypothesis that small modifications to the objects' velocity in the periphery and geometrical distortions in the peripheral vision can remain unnoticeable yet lead to reduced vection. This paper describes the experiments supporting this hypothesis and derives its limits. Furthermore, we present a method that exploits these findings by introducing subtle, screen-space geometrical distortions to animation frames to counteract the motion contributing to vection. We implement the method as a realtime post-processing step that can be integrated into existing rendering frameworks. The final validation of the technique and comparison to an alternative approach confirms its effectiveness in reducing cybersickness.
Bilateral Guided Radiance Field Processing
Authors: Wang Yuehao , Wang Chaoyi , Gong Bingchen , Xue Tianfan
Published: July 2024

Neural Radiance Fields (NeRF) achieves unprecedented performance in synthesizing novel view synthesis, utilizing multi-view consistency. When capturing multiple inputs, image signal processing (ISP) in modern cameras will independently enhance them, including exposure adjustment, color correction, local tone mapping, etc. While these processings greatly improve image quality, they often break the multi-view consistency assumption, leading to "floaters" in the reconstructed radiance fields. To address this concern without compromising visual aesthetics, we aim to first disentangle the enhancement by ISP at the NeRF training stage and re-apply user-desired enhancements to the reconstructed radiance fields at the finishing stage. Furthermore, to make the re-applied enhancements consistent between novel views, we need to perform imaging signal processing in 3D space (i.e. "3D ISP"). For this goal, we adopt the bilateral grid, a locally-affine model, as a generalized representation of ISP processing. Specifically, we optimize per-view 3D bilateral grids with radiance fields to approximate the effects of camera pipelines for each input view. To achieve user-adjustable 3D finishing, we propose to learn a low-rank 4D bilateral grid from a given single view edit, lifting photo enhancements to the whole 3D scene. We demonstrate our approach can boost the visual quality of novel view synthesis by effectively removing floaters and performing enhancements from user retouching. The source code and our data are available at: https://bilarfpro.github.io.
Cyclogenesis: Simulating Hurricanes and Tornadoes
Authors: Amador Herrera Jorge Alejandro , Klein Jonathan , Liu Daoming , Pałubicki Wojtek , Pirk Sören , Michels Dominik L.
Published: July 2024

Cyclones are large-scale phenomena that result from complex heat and water transfer processes in the atmosphere, as well as from the interaction of multiple
hydrometeors
, i.e., water and ice particles. When cyclones make landfall, they are considered natural disasters and spawn dread and awe alike. We propose a physically-based approach to describe the 3D development of cyclones in a visually convincing and physically plausible manner. Our approach allows us to capture large-scale heat and water continuity, turbulent microphysical dynamics of hydrometeors, and mesoscale cyclonic processes within the planetary boundary layer. Modeling these processes enables us to simulate multiple hurricane and tornado phenomena. We evaluate our simulations quantitatively by comparing to real data from storm soundings and observations of hurricane landfall from climatology research. Additionally, qualitative comparisons to previous methods are performed to validate the different parts of our scheme. In summary, our model simulates cyclogenesis in a comprehensive way that allows us to interactively render animations of some of the most complex weather events.
Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning
Authors: Liu Yilin , Chen Jiale , Pan Shanshan , Cohen-Or Daniel , Zhang Hao , Huang Hui
Published: July 2024

We introduce a novel method for acquiring boundary representations (B-Reps) of 3D CAD models which involves a two-step process: it first applies aspatial partitioning, referred to as the "split", followed by a "fit" operation to derive a single primitive within each partition. Specifically, our partitioning aims to produce the classicalVoronoi diagramof the set of ground-truth (GT) B-Rep primitives. In contrast to prior B-Rep constructions which were bottom-up, either via direct primitive fitting or point clustering, our Split-and-Fit approach istop-downandstructure-aware, since a Voronoi partition explicitly reveals both the number of and the connections between the primitives. We design a neural network to predict the Voronoi diagram from an input point cloud or distance field via a binary classification. We show that our network, coined NVD-Net for neural Voronoi diagrams, can effectively learn Voronoi partitions for CAD models from training data and exhibits superior generalization capabilities. Extensive experiments and evaluation demonstrate that the resulting B-Reps, consisting of parametric surfaces, curves, and vertices, are more plausible than those obtained by existing alternatives, with significant improvements in reconstruction quality. Code will be released on https://github.com/yilinliu77/NVDNet.
3Doodle: Compact Abstraction of Objects with 3D Strokes
Authors: Choi Changwoon , Lee Jaeah , Park Jaesik , Kim Young Min
Published: July 2024

While free-hand sketching has long served as an efficient representation to convey characteristics of an object, they are often subjective, deviating significantly from realistic representations. Moreover, sketches are not consistent for arbitrary viewpoints, making it hard to catch 3D shapes. We propose 3Dooole, generating descriptive and view-consistent sketch images given multi-view images of the target object. Our method is based on the idea that a set of 3D strokes can efficiently represent 3D structural information and render view-consistent 2D sketches. We express 2D sketches as a union of view-independent and view-dependent components. 3D cubic Bézier curves indicate view-independent 3D feature lines, while contours of superquadrics express a smooth outline of the volume of varying viewpoints. Our pipeline directly optimizes the parameters of 3D stroke primitives to minimize perceptual losses in a fully differentiable manner. The resulting sparse set of 3D strokes can be rendered as abstract sketches containing essential 3D characteristic shapes of various objects. We demonstrate that 3Doodle can faithfully express concepts of the original images compared with recent sketch generation approaches.
1
CWF: Consolidating Weak Features in High-quality Mesh Simplification
Authors: Xu Rui , Liu Longdu , Wang Ningna , Chen Shuangmin , Xin Shiqing , Guo Xiaohu , Zhong Zichun , Komura Taku , Wang Wenping , Tu Changhe
Published: July 2024

In mesh simplification, common requirements like accuracy, triangle quality, and feature alignment are often considered as a trade-off. Existing algorithms concentrate on just one or a few specific aspects of these requirements. For example, the well-known Quadric Error Metrics (QEM) approach [Garland and Heckbert 1997] prioritizes accuracy and can preserve strong feature lines/points as well, but falls short in ensuring high triangle quality and may degrade weak features that are not as distinctive as strong ones. In this paper, we propose a smooth functional that simultaneously considers all of these requirements. The functional comprises a normal anisotropy term and a Centroidal Voronoi Tessellation (CVT) [Du et al. 1999] energy term, with the variables being a set of movable points lying on the surface. The former inherits the spirit of QEM but operates in a continuous setting, while the latter encourages even point distribution, allowing various surface metrics. We further introduce a decaying weight to automatically balance the two terms. We selected 100 CAD models from the ABC dataset [Koch et al. 2019], along with 21 organic models, to compare the existing mesh simplification algorithms with ours. Experimental results reveal an important observation: the introduction of a decaying weight effectively reduces the conflict between the two terms and enables the alignment of weak features. This distinctive feature sets our approach apart from most existing mesh simplification methods and demonstrates significant potential in shape understanding. Please refer to the teaser figure for illustration.
Stylized Rendering as a Function of Expectation
Authors: West Rex , Mukherjee Sayan
Published: July 2024

We propose a generalization of the rendering equation that captures both the realistic light transport of physically-based rendering (PBR) and a subset of non-photorealistic rendering (NPR) stylizations in a principled manner. The proposed formulation is based on the key observation that both classical transport and certain NPR stylizations can be modeled as a
function of expectation.
Given this observation, we generalize the recursive integrals of the rendering equation to
recursive functions of expectation.
As estimating functions of expectation can be challenging, especially recursive ones, we provide a toolkit for unbiased and biased estimation comprising prior work, general strategies, and a novel build-your-own strategy for constructing more complex unbiased estimators from simpler unbiased estimators. We then use this toolkit to construct a complete estimator for the proposed recursive formulation, and implement a sampling algorithm that is both conceptually simple and leverages many of the components of an ordinary path tracer. To demonstrate the practicality of the proposed method we showcase how it captures several existing stylizations like color mapping, cel shading, and cross-hatching, fuses NPR and PBR visuals, and allows us to explore visuals that were previously challenging under existing formulations.
Creating LEGO Figurines from Single Images
Authors: Ge Jiahao , Zhou Mingjun , Bao Wenrui , Xu Hao , Fu Chi-Wing
Published: July 2024

This paper presents a computational pipeline for creating personalized, physical LEGO
®1
figurines from user-input portrait photos. The generated figurine is an assembly of coherently-connected LEGO
®
bricks detailed with uv-printed decals, capturing prominent features such as hairstyle, clothing style, and garment color, and also intricate details such as logos, text, and patterns. This task is non-trivial, due to the substantial domain gap between unconstrained user photos and the stylistically-consistent LEGO
®
figurine models. To ensure assemble-ability by LEGO
®
bricks while capturing prominent features and intricate details, we design a three-stage pipeline: (i) we formulate a CLIP-guided retrieval approach to connect the domains of user photos and LEGO
®
figurines, then output physically-assemble-able LEGO
®
figurines with decals excluded; (ii) we then synthesize decals on the figurines via a symmetric U-Nets architecture conditioned on appearance features extracted from user photos; and (iii) we next reproject and uv-print the decals on associated LEGO
®
bricks for physical model production. We evaluate the effectiveness of our method against eight hundred expert-designed figurines, using a comprehensive set of metrics, which include a novel GPT-4V-based evaluation metric, demonstrating superior performance of our method in visual quality and resemblance to input photos. Also, we show our method's robustness by generating LEGO
®
figurines from diverse inputs and physically fabricating and assembling several of them.
NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature
Authors: Dong Qiujie , Xu Rui , Wang Pengfei , Chen Shuangmin , Xin Shiqing , Jia Xiaohong , Wang Wenping , Tu Changhe
Published: July 2024

Despite recent advances in reconstructing an organic model with the neural signed distance function (SDF), the high-fidelity reconstruction of a CAD model directly from low-quality unoriented point clouds remains a significant challenge. In this paper, we address this challenge based on the prior observation that the surface of a CAD model is generally composed of piecewise surface patches, each approximately developable even around the feature line. Our approach, named
NeurCADRecon
, is self-supervised, and its loss includes a developability term to encourage the Gaussian curvature toward 0 while ensuring fidelity to the input points (see the teaser figure). Noticing that the Gaussian curvature is non-zero at tip points, we introduce a double-trough curve to tolerate the existence of these tip points. Furthermore, we develop a dynamic sampling strategy to deal with situations where the given points are incomplete or too sparse. Since our resulting neural SDFs can clearly manifest sharp feature points/lines, one can easily extract the feature-aligned triangle mesh from the SDF and then decompose it into smooth surface patches, greatly reducing the difficulty of recovering the parametric CAD design. A comprehensive comparison with existing state-of-the-art methods shows the significant advantage of our approach in reconstructing faithful CAD shapes.
S3: Speech, Script and Scene driven Head and Eye Animation
Authors: Pan Yifang , Agrawal Rishabh , Singh Karan
Published: July 2024

We present
S
3
, a novel approach to generating expressive, animator-centric 3D head and eye animation of characters in conversation. Given
speech
audio, a Directorial
script
and a cinematographic 3D
scene
as input, we automatically output the animated 3D rotation of each character's head and eyes.
S
3
distills animation and psycho-linguistic insights into a novel modular framework for conversational gaze capturing: audio-driven rhythmic head motion; narrative script-driven emblematic head and eye gestures; and gaze trajectories computed from audio-driven gaze focus/aversion and 3D visual scene salience. Our evaluation is four-fold: we quantitatively validate our algorithm against ground truth data and baseline alternatives; we conduct a perceptual study showing our results to compare favourably to prior art; we present examples of animator control and critique of
S
3
output; and present a large number of compelling and varied animations of conversational gaze.
FlexScale: Modeling and Characterization of Flexible Scaled Sheets
Authors: Montes Maestre Juan Sebastian , Du Yinwei , Hinchet Ronan , Coros Stelian , Thomaszewski Bernhard
Published: July 2024

We present a computational approach for modeling the mechanical behavior of flexible scaled sheet materials---3D-printed hard scales embedded in a soft substrate. Balancing strength and flexibility, these structured materials find applications in protective gear, soft robotics, and 3D-printed fashion. To unlock their full potential, however, we must unravel the complex relation between scale pattern and mechanical properties. To address this problem, we propose a contact-aware homogenization approach that distills native-level simulation data into a novel macromechanical model. This macro-model combines piecewise-quadratic uniaxial fits with polar interpolation using circular harmonics, allowing for efficient simulation of large-scale patterns. We apply our approach to explore the space of isohedral scale patterns, revealing a diverse range of anisotropic and nonlinear material behaviors. Through an extensive set of experiments, we show that our models reproduce various scale-level effects while offering good qualitative agreement with physical prototypes on the macro-level.
Real-time Physically Guided Hair Interpolation
Authors: Hsu Jerry , Wang Tongtong , Pan Zherong , Gao Xifeng , Yuksel Cem , Wu Kui
Published: July 2024

Strand-based hair simulations have recently become increasingly popular for a range of real-time applications. However, accurately simulating the full number of hair strands remains challenging. A commonly employed technique involves simulating a subset of guide hairs to capture the overall behavior of the hairstyle. Details are then enriched by interpolation using linear skinning. Hair interpolation enables fast real-time simulations but frequently leads to various artifacts during runtime. As the skinning weights are often pre-computed, substantial variations between the initial and deformed shapes of the hair can cause severe deviations in fine hair geometry. Straight hairs may become kinked, and curly hairs may become zigzags.

This work introduces a novel physical-driven hair interpolation scheme that utilizes existing simulated guide hair data. Instead of directly operating on positions, we interpolate the internal forces from the guide hairs before efficiently reconstructing the rendered hairs based on their material model. We formulate our problem as a constraint satisfaction problem for which we present an efficient solution. Further practical considerations are addressed using regularization terms that regulate penetration avoidance and drift correction. We have tested various hairstyles to illustrate that our approach can generate visually plausible rendered hairs with only a few guide hairs and minimal computational overhead, amounting to only about 20% of conventional linear hair interpolation. This efficiency underscores the practical viability of our method for real-time applications.
Vertex Block Descent
Authors: Chen Anka He , Liu Ziheng , Yang Yin , Yuksel Cem
Published: July 2024

We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global variational energy with maximized parallelism. This forms a physics solver that can achieve numerical convergence with unconditional stability and exceptional computation performance. It can also fit in a given computation budget by simply limiting the iteration count while maintaining its stability and superior convergence rate.

We present and evaluate our method in the context of elastic body dynamics, providing details of all essential components and showing that it outperforms alternative techniques. In addition, we discuss and show examples of how our method can be used for other simulation systems, including particle-based simulations and rigid bodies.
Text-to-Vector Generation with Neural Path Representation
Authors: Zhang Peiying , Zhao Nanxuan , Liao Jing
Published: July 2024

Vector graphics are widely used in digital art and highly favored by designers due to their scalability and layer-wise properties. However, the process of creating and editing vector graphics requires creativity and design expertise, making it a time-consuming task. Recent advancements in text-to-vector (T2V) generation have aimed to make this process more accessible. However, existing T2V methods directly optimize control points of vector graphics paths, often resulting in intersecting or jagged paths due to the lack of geometry constraints. To overcome these limitations, we propose a novel neural path representation by designing a dual-branch Variational Autoencoder (VAE) that learns the path latent space from both sequence and image modalities. By optimizing the combination of neural paths, we can incorporate geometric constraints while preserving expressivity in generated SVGs. Furthermore, we introduce a two-stage path optimization method to improve the visual and topological quality of generated SVGs. In the first stage, a pre-trained text-to-image diffusion model guides the initial generation of complex vector graphics through the Variational Score Distillation (VSD) process. In the second stage, we refine the graphics using a layer-wise image vectorization strategy to achieve clearer elements and structure. We demonstrate the effectiveness of our method through extensive experiments and showcase various applications. The project page is https://intchous.github.io/T2V-NPR.
Interactive Invigoration: Volumetric Modeling of Trees with Strands
Authors: Li Bosheng , Schwarz Nikolas Alexander , Pałubicki Wojtek , Pirk Sören , Benes Bedrich
Published: July 2024

Generating realistic models of trees and plants is a complex problem because of the vast variety of shapes trees can form. Procedural modeling algorithms are popular for defining branching structures and steadily increasing their expressive power by considering more biological findings. Most existing methods focus on defining the branching structure of trees based on skeletal graphs, while the surface mesh of branches is most commonly defined as simple cylinders. One critical open problem is defining and controlling the complex details observed in real trees. This paper aims to advance tree modeling by proposing a strand-based volumetric representation for tree models. Strands are fixed-size volumetric pipes that define the branching structure. By leveraging strands, our approach captures the lateral development of trees. We combine the strands with a novel branch development formulation that allows us to locally inject vigor and reshape the tree model. Moreover, we define a set of editing operators for tree primary and lateral development that enables users to interactively generate complex tree models with unprecedented detail with minimal effort.
Smooth Bijective Projection in a High-order Shell
Authors: Liu Shibo , Ji Yang , Guo Jia-Peng , Liu Ligang , Fu Xiao-Ming
Published: July 2024

We propose a new structure called a higher-order shell, which is composed of a set of triangular prisms. Each triangular prism is enveloped by three Bézier triangles (top, middle, and bottom) and three side surfaces, each of which is trimmed from a bilinear surface. Moreover, we define a continuous vector field to smoothly and bijectively transfer attributes between two surfaces inside the shell. Since the higher-order shell has several hard construction constraints, we apply an interior-point strategy to robustly and automatically construct a high-order shell for an input mesh. Specifically, the strategy starts from a valid linear shell with a small thickness. Then, the shell is optimized until the specified thickness is reached, where explicit checks ensure that the constraints are always satisfied. We extensively test our method on more than 8300 models, demonstrating its robustness and performance. Compared to state-of-the-art methods, our bijective projection is smoother, and the space between the shell and input mesh is more uniform.
Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT
Authors: Su Fujia , Li Bingxuan , Yin Qingyang , Zhang Yanchen , Li Sheng
Published: July 2024

Robust light transport algorithms, particularly bidirectional path tracing (BDPT), face significant challenges when dealing with specular or highly glossy involved paths. BDPT constructs the full path by connecting sub-paths traced individually from the light source and camera. However, it remains difficult to sample by connecting vertices on specular and glossy surfaces with narrow-lobed BSDF, as it poses severe constraints on sampling in the feasible direction. To address this issue, we propose a novel approach, called
proxy sampling
, that enables efficient sub-path connection of these challenging paths. When a low-contribution specular/glossy connection occurs, we drop out the problematic neighboring vertex next to this specular/glossy vertex from the original path, then retrace an alternative sub-path as a proxy to complement this incomplete path. This newly constructed complete path ensures that the connection adheres to the constraint of the narrow lobe within the BSDF of the specular/glossy surface. Unbiased reciprocal estimation is the key to our method to obtain a probability density function (PDF) reciprocal to ensure unbiased rendering. We derive the reciprocal estimation method and provide an efficiency-optimized setting for efficient sampling and connection. Our method provides a robust tool for substituting problematic paths with favorable alternatives while ensuring unbiasedness. We validate this approach in the probabilistic connections BDPT for addressing specular-involved difficult paths. Experimental results have proved the effectiveness and efficiency of our approach, showcasing high-performance rendering capabilities across diverse settings.