Search Results (132)

Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an explicit surface anchor. In this paper, we present H²Avatar, a real-time framework that builds on a mesh-embedded 3D Gaussian representation guided by SMPL-X and disentangles geometry and appearance into hierarchical and hybrid components. For geometry, we propose a semantic-aware hierarchical encoding based on a multi-scale tri-plane pyramid, where features at different resolutions capture both global structure and high-frequency surface details such as clothing wrinkles. For appearance, we introduce a hybrid rendering strategy that anchors canonical colors using a learnable UV texture map, and complements it with a neural residual color branch conditioned on tri-plane features, pose embedding, and surface normals to model pose- and view-dependent shading variations. This design improves temporal stability and preserves identity details while enhancing photorealism under complex motions. Experiments on the NeuMan dataset demonstrate that H²Avatar consistently outperforms representative baselines across multiple sequences, outperforming ExAvatar by up to 0.66 dB in PSNR and reducing LPIPS by up to 16.3%. These results validate the effectiveness of hierarchical geometry encoding and texture-anchored hybrid appearance modeling. Full article

Automated recognition of rock mass discontinuities in vegetated high-slope terrains remains a challenging task critical to geohazard assessment and slope stability analysis. This study presents an integrated framework combining close-range UAV photogrammetry with an Improved Superpoint Transformer (ISPT) for semantic segmentation and structural characterization. High-resolution UAV imagery was processed using an SfM–MVS photogrammetric workflow to generate dense point clouds, followed by a three-stage filtering workflow comprising cloth simulation filtering, volumetric density analysis, and VDVI-based vegetation discrimination. Feature augmentation using volumetric density and the Visible-Band Difference Vegetation Index (VDVI), together with connected-component segmentation, enhanced robustness under vegetation occlusion. Validation on four vegetated slopes in Buyun Mountain, China, achieved an overall classification accuracy of 89.5%, exceeding CANUPO (78.2%) and the baseline SPT (85.8%), with a 25-fold improvement in computational efficiency. In total, 4918 structural planes were extracted, and their orientations, dip angles, and trace lengths were automatically derived. The proposed ISPT-based framework provides an efficient and reliable approach for high-precision geotechnical characterization in complex, vegetation-covered rock mass environments. Full article

The consumption of clothes creates paradoxes in which values, motives, and emotions interact to generate consumption experiences. To test some of these interactions, we conducted three correlational studies, studies 1, 2, and 3, one experiment, study 4, and one qualitative study, study 5. Study 1 found negative relationships between sustainability values and materialism and positive relationships between sustainable values and the preference for experiential purchases. Study 2 found positive relationships between two components of the slow-fashion movement, equity and exclusiveness, and guilt, and a negative relationship with functionality, another component of slow fashion. Study 3 found an indirect relationship between sustainable values and guilt through their positive and significant relationship with increased awareness of the environmental impact of the fast-fashion industry, supporting a mediation model. Study 4 found that participants were was more likely, regardless of whether the purchase of clothing was labeled as fast fashion or not, to experience pride than guilt when recalling recent past purchases. Last, in study 5, we found that consumers buy clothes to look good and pay attention to quality and value without significant concerns for environmental issues. The implications for consumer behavior were discussed. Full article

With the rapid advancement of modern electronic devices and wireless communication systems, electromagnetic pollution has become a prominent issue, prompting the development of high-performance electromagnetic interference (EMI) shielding materials. Although traditional metal shielding materials exhibit excellent conductivity, there are many limitations such as high weight, poor flexibility, susceptibility to corrosion, and high cost. To overcome these challenges, in this study, we design and fabricate core-spun yarns using polyester filaments as the core and an aluminum-foil-wrapped layer as the conductive outer component, and further weave them into three conductive fabrics with different structural parameters. Through systematic investigation of their surface morphology, air permeability, electrical properties, and EMI shielding performance, DT5W27 demonstrates optimal overall performance: electrical conductivity of 2722.64 S·m⁻¹, shielding effectiveness of 37.29 dB, and electromagnetic wave attenuation rate of 99.99%. Specifically, even after 100 bending, twisting cycles, and exposure to solutions with pH values ranging from 3 to 9, the fabric maintains high shielding performance. The fabrication process is facile and low cost, and these composites have good flexibility, outstanding EMI shielding performance, exceptional mechanical durability, and chemical stability. These advantages make them have broad application potential in protective clothing and lightweight shielding materials. Full article

Sweet bags were small, embroidered textile pouches used in the sixteenth and seventeenth centuries to carry fragrant substances, money, books, sewing tools, mirrors, or other personal items. They were often exchanged as gifts, used to preserve clothing in wardrobes, or used to protect against contaminated air. Beyond their material function, both their name and some of their uses suggest an olfactory dimension, as they were typically filled with aromatic herbs—combinations frequently recorded in recipe books, medical, and household manuals, including Countrey Contentments, or The English Husvvife, Praxis Medicinæ, or The Physitian’s Practise, and Exenterata, among others. Through close reading and literary analysis of such primary sources combined with a sensory approach, this article traces the possible ingredients of these pouches in Early Modern recipes and argues that their olfactory content positions them as objects of the “olfactory gaze” (Verbeek), thereby transforming them into elements of olfactory heritage. Ultimately, the article seeks to recreate the olfactory component of sweet bags within recipe-related practices, and broader domestic traditions of Early Modern England. Full article

This study investigates the anti-cut properties of a composite based on basalt fabrics with varied structural characteristics, including weave and thread density, enhanced with reduced graphene oxide (rGO). The primary aim is to evaluate the potential of integrating rGO into a basalt matrix to improve its resistance to cutting and mechanical damage. The results indicate that adding rGO significantly increases the cutting resistance of the composite. Molecular simulations demonstrate that the composite, which combines a cross-linked LG 700 resin, rGO, and basalt, is one of the most thermodynamically stable configurations due to strong electrostatic interactions between its components. These interactions and the formation of physical bonds at the interfaces stiffen the material, while also allowing for a unique crack-toughening effect. This resilience, which enables the reformation of physical interactions after stress, directly contributes to the composite’s enhanced resistance to catastrophic failure and its observed performance in cutting tests. These findings suggest that basalt–resin with rGO composites hold great potential for applications requiring high mechanical strength and durability, such as protective clothing (e.g., gloves) and anti-vandalism materials. The study concludes that the developed composite represents a promising advancement for materials exposed to cutting forces. Full article

This paper reports the spinning and wear comfort properties of polyethylene terephthalate (PET)/copolymer-PET (Co-PET) hollow yarns and their fabrics, as well as the effect of the wt.% of inorganic particles embedded in the core of the bicomponent yarns. The results are discussed in terms of the types and amounts of inorganic particles (titanium dioxide (TiO₂) and calcium carbonate (CaCO₃)) embedded in the sheath of the bi-component yarns (Kolon semi-dull (KSD), Kolon full-dull (KFD), and Kolon calcium carbonate (KCC) PET/Co-PET yarns). The three sheath/3-core bicomponent yarns developed in this study exhibited good spinnability and weavability with relatively strong tenacity and breaking strain. Their optimal spinning conditions were determined. The KCC PET/Co-PET fabric showed the greatest hollowness ratio, followed by the KFD PET/Co-PET and KSD PET/Co-PET fabrics. This might be attributed to the higher wt.% (2.5 wt.%) of CaCO₃ particles embedded in the sheath of the KCC PET/Co-PET yarns and to the larger particle size (0.8 μm) of CaCO₃. Regarding the wear comfort, the moisture management system (MMT) test indicated that the KFD PET/Co-PET fabric is suitable for market applications because of its good moisture absorption and rapid drying. The KFD PET/Co-PET fabric is useful for winter clothing applications because of its relatively high heat retention rate and lack of durability issues with washing. An examination of the wearing performance for fitness with a tactile hand feel showed that KFD and KCC/Co-PET fabrics imparted a softer tactile hand feel than the KSD PET/Co-PET fabric. On the other hand, the KCC PET/Co-PET fabric was assumed to have some issues with wearing durability. Full article

The growing amount of waste worldwide requires new solutions for its management. Agricultural by-products account for almost 10% of the waste generated. One of them is sheep wool, a natural fibre with beneficial physicochemical properties. Currently, sheep wool production amounts to approximately 1–2 million tonnes per year, of which 60% is used in the manufacture of clothing. Nevertheless, it poses a considerable challenge in terms of disposal due to its keratin-rich composition and slow biodegradability. This review analyses the chemical and physical properties of sheep wool and assesses its potential as biomass based on its carbon content and other elemental components. This allows us to provide a critical comparative analysis of the main technological pathways for the use of waste sheep wool as biomass, including anaerobic digestion, pyrolysis, direct combustion and gasification. The review highlights both the opportunities and limitations of these processes, comparing sheep wool in terms of energy potential and carbon footprint with other biomass. The review shows that the calorific value of sheep wool (19.5 MJ/kg) is competitive with traditional plant-based biofuels and the use of waste sheep wool as biomass source can contribute to reduction in CO₂ emissions of 2.1 million tonnes per year. The use of sheep wool as biomass can not only contribute to waste reduction but also supports the goals of sustainable agriculture and climate neutrality. The selected methods may offer a new and effective way of reducing waste and allow all sheep wool produced to be introduced into the circular economy. Full article

This paper explores the feasibility of using a wireless Inductive Resonant Link (IRL) for wearable posture monitoring. The proposed system is based on magnetically coupled textile resonators and is implemented using a Single Input Multiple Output (SIMO) configuration. In particular, the setup consists of four inductively coupled resonators: one transmitting coil integrated into a textile structure and positioned on the back of the neck, and three receiving coils placed on the shoulders. The magnetic coupling between these elements varies as a function of the user’s posture, making it possible to monitor postural changes by analyzing variations in the transmission coefficients of the link. Unlike traditional sensor-based systems that require multiple components and data processing, the proposed method uses the inherent response of the inductive link to detect posture in a simple and efficient way. To validate the concept, experimental measurements of the scattering parameters were carried out using a compact and low-power vector network analyzer. The results show a consistent and measurable relationship between postural changes and variations in the transmission coefficients, demonstrating the effectiveness of the proposed system in distinguishing between different postures. The findings suggest that inductive resonant wireless links, especially when implemented with textile components, represent a promising alternative to traditional wearable sensor technologies for posture tracking. The approach offers significant advantages in terms of wearability, power consumption, and simplicity, making it suitable for applications in ergonomics, rehabilitation, occupational health, and smart clothing. Full article

Driven by growing public security demands and the advancement of intelligent surveillance systems, person re-identification (ReID) has emerged as a prominent research focus in the field of computer vision. However, this task presents challenges due to its high sensitivity to variations in visual appearance caused by factors such as body pose and camera parameters. Although deep learning-based methods have achieved marked progress in ReID, the high cost of annotation remains a challenge that cannot be overlooked. To address this, we propose an unsupervised attribute learning framework that eliminates the need for costly manual annotations while maintaining high accuracy. The framework learns the mid-level human attributes (such as clothing type and gender) that are robust to substantial visual appearance variations and can hence boost the accuracy of attributes with a small amount of labeled data. To carry out our framework, we present a part-based convolutional neural network (CNN) architecture, which consists of two components for image and body attribute learning on a global level and upper- and lower-body image and attribute learning at a local level. The proposed architecture is trained to learn attribute-semantic and identity-discriminative feature representations simultaneously. For model learning, we first train our part-based network using a supervised approach on a labeled attribute dataset. Then, we apply an unsupervised clustering method to assign pseudo-labels to unlabeled images in a target dataset using our trained network. To improve feature compatibility, we introduce an attribute consistency scheme for unsupervised domain adaptation on this unlabeled target data. During training on the target dataset, we alternately perform three steps: extracting features with the updated model, assigning pseudo-labels to unlabeled images, and fine-tuning the model. Through a unified framework that fuses complementary attribute-label and identity label information, our approach achieves considerable improvements of 10.6% and 3.91% mAP on Market-1501→DukeMTMC-ReID and DukeMTMC-ReID→Market-1501 unsupervised domain adaptation tasks, respectively. Full article

The localization and pose estimation of clinicians in the operating room is a critical component for building intelligent perception systems, playing a vital role in enhancing surgical standardization and safety. Multi-view, multi-person 3D pose estimation is a highly challenging task—especially in the operating room, where the presence of sterile clothing, occlusion from surgical instruments, and limited data availability due to privacy concerns exacerbate the difficulty. While voxel-based 3D pose estimation methods have shown promising results in general scenarios, their performance is significantly challenged in surgical environments with limited camera views and severe occlusions. To address these issues, this paper proposes a fine-grained voxel feature reconstruction method enhanced with depth information, effectively mitigating projection errors caused by reduced viewpoints. Additionally, an attention mechanism is integrated into the encoder–decoder architecture to improve the network’s capacity for global information modeling and enhance the accuracy of keypoint regression. Experiments conducted in real-world operating room scenarios, using the Multi-View Operating Room (MVOR) dataset, demonstrate that the proposed method maintains high accuracy even under limited camera views and outperforms existing state-of-the-art multi-view 3D pose estimation approaches. This work provides a novel and efficient solution for human pose estimation (HPE) in complex medical environments. Full article

The energy self-sufficiency of wastewater treatment plants has become an essential aspect of sustainable water and energy resource management. On the other hand, due to the expansion of urban conglomerations and agricultural activities, as well as more frequent and erratic meteorological phenomena (e.g., droughts), the majority of EU nations are confronted with water scarcity and the deterioration of water quality. As a consequence, EU member states pledged to implement “tertiary treatment” in all municipal wastewater treatment facilities by the end of 2040. This publication presents an analysis of the efficiency of an energy-efficient gravity cloth disk filter used for treating municipal wastewater in a treatment plant located in a tourist resort in Poland, operating under variable hydraulic loading conditions. Gravity cloth disk filters appear to be the least energy-consuming. The energy consumption of disk filters was 13 Wh/m³ in 2024. The filter ensures the leveling of disturbances in the operation of earlier treatment stages, particularly in terms of retaining total suspended solids (TSSs). The achieved efficiency of TSS removal was 45%. The TSS value in the outflow from the filter did not exceed the limit value from the permit (35 mg/L). When operated correctly, additional filtration and disinfection may become essential components of a wastewater treatment plant, enabling the achievement of wastewater quality that supports water recovery for technological and agricultural purposes, particularly in small, non-industrial areas. They should also consume less energy than other advanced technologies used in the third and fourth stages of wastewater treatment. Full article

The research was initiated by the Plečnik House gallery (Ljubljana, Slovenia) and focuses on the 3D architectural reconstruction of the unrealised monument of the Czech military leader Jan Žižka, designed by the Slovenian architect Jože Plečnik. In addition, the experience with the 3D reconstructed monument in the exhibition “Plečnik and the Sacred” was analysed. Using the available references and interpretative approaches, a digital and 3D-printed reconstruction was created that retains Plečnik’s architectural style. The experimental phase included a detailed interpretation of the studied references, 3D modelling, 3D printing, exhibition and experience analysis. The dimensions of the finished 3D-printed model are 52.80 × 55.21 × 44.60 cm. It was produced using stereolithography (SLA) for figurative elements and fused deposition modelling (FDM) for architectural components. The reconstruction was evaluated using participant testing, including semantic differential analysis, comparative studies, and knowledge-based questionnaires. The results showed that architectural elements were reconstructed with an average similarity score of 1.97 out of 5. Statues followed with a score of 1.81, and props, though detailed, met audience expectations, scoring 1.61. Clothing received the lowest score of 1.40. This research emphasises the importance of a hypothetical digital 3D reconstruction of never constructed monument for broader understanding of Plečnik’s legacy. Full article

The aim of this study was to elucidate all the degradation byproducts (DBPs) of bemotrizinol (BEMT) that are associated with sodium hypochlorite treatment. BEMT is a UV filter that is found not only in many personal care products, such as sunscreen and cosmetics, but also as an additive in plastics or clothing to protect them from damage that results from absorbed radiation. BEMT has been detected in wastewater, surface water, and some lake sediments, in quantities from a few ng/L to hundreds of ng/L, to such an extent that, today, it is considered an emerging pollutant. In this study, the UV filter was subjected to oxidation with sodium hypochlorite, which is an oxidant at the base of the disinfection process that is used in most wastewater treatment plants or in swimming pools. Using different chromatographic methods (CC, TLC, HPLC, and GC), the resulting DBP mixture was separated into its main components, which were then identified using one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. Nineteen DBPs were isolated, and a plausible reaction mechanism was proposed to explain how they were obtained. Full article

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 × 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 × 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity’s built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments. Full article