Leather is among the most widely traded textile materials worldwide, and detecting surface defects is crucial to ensuring its quality. Conventional manual inspection of leather surfaces is labor-intensive, time-consuming, and prone to human error. Advances in computer vision have facilitated the development of automated leather surface defect detection. However, vision-based methods remain sensitive to lighting conditions, particularly on high-gloss leather surfaces. In this paper, we introduce Le-Radio, an industrial IoT (IIoT) oriented RF-based system for automated leather defect detection. Le-Radio provides a gloss-robust, ubiquitous, and continuous inspection solution, which is crucial for integrating quality control into smart factory pipelines and advancing the digital transformation of the traditional leather industry. To extract informative representations of leather defects from wireless signals, we employ continuous radio snapshots and refined signal features to differentiate defect patterns. Specifically, we design an imaging algorithm that continuously visualizes defects on moving leather samples using a fixed radar. To enhance Le-Radio’s robustness in diverse environments, we train a transferable model that maintains consistent detection performance across different scenarios. We conduct extensive experiments in three rooms using 120 leather samples to evaluate Le-Radio. Experimental results demonstrate that Le-Radio accurately detects diverse defects in high-gloss leather. Beyond leather inspection, this work establishes Le-Radio as a practical IoT edge sensing node for material surface inspection in the IIoT. By integrating RF-based defect scanning capabilities and a robust radar imaging framework into a unified system, this work establishes RF sensing as a viable and novel paradigm for material surface inspection in the IIoT, particularly in scenarios where optical methods are fundamentally limited.

ABSTRACT Self‐collision handling remains a fundamental and long‐standing challenge in neural cloth simulation, particularly for loose garments with complex topologies. We propose a self‐supervised neural cloth simulation framework that integrates Gaussian mixture skinning (GMS) with a differentiable collision‐overlap loss to significantly enhance physical plausibility and visual realism. We employ continuous and spatially smooth GMS weights to model vertex‐skeleton coupling, enabling stable deformations under large body motions. To explicitly address cloth self‐collisions, we introduce a differentiable spatial repulsion constraint that suppresses interpenetration and layer‐overlap artifacts. The proposed objective is jointly optimized with physics‐inspired losses, enabling the network to learn consistent cloth dynamics without relying on ground‐truth physical simulations. Experimental results demonstrate improved temporal stability, reduced collision artifacts, and stronger generalization compared to existing self‐supervised methods.

Reconstructing transparent objects with high fidelity presents significant challenges due to complex light refraction and reflection. Existing methods rely on intentionally designed patterns observed behind the transparent object to infer the correspondence between rays and the background, thereby improving the precision of the reconstruction. However, they are hindered by a refraction-tracing-based strategy that fails to reconstruct complex nested transparent objects and a tedious view-capture strategy relying on images captured from empirically determined viewpoints. To overcome these obstacles, we propose AHC-NeRF, an autonomous, high-quality neural SDF-based framework designed for reconstructing two-layer complex nested transparent objects. Firstly, our framework combines neural SDF with single-pixel imaging, a reflection-based method, which utilizes point-pair priors as guidance to achieve high-quality reconstruction of both the outer and inner surfaces. Secondly, we propose an adaptive single-pixel imaging method that achieves an acceleration of 1-2 orders of magnitude compared to vanilla single-pixel imaging for the acquisition of point-pair priors. Finally, we introduce a novel view-planning strategy that progressively identifies the viewpoints with the highest information gain throughout the optimization process, thereby achieving high-quality surface reconstruction. Extensive experimental results on both synthetic and real-world datasets demonstrate that AHC-NeRF outperforms state-of-the-art methods.

To address the efficiency bottlenecks in hierarchical GNN-based cloth simulation, we propose two targeted architectural components that jointly optimize message propagation and topological redundancy. Specifically, we introduce a Redundant Message Reduction strategy that streamlines cloth-body interactions by restricting communication to only necessary stages, and a Proportional Edge Pruning mechanism that adaptively reduces the number of incoming edges per node while retaining sufficient deformation paths. These designs substantially reduce computational overhead with minimal sacrifice of physical fidelity. Extensive experiments across diverse garments and motion sequences demonstrate that our model achieves more than a 40% increase in inference speed and a 22% reduction in training time, while ensuring both numerical and visual fidelity. In addition, qualitative evaluations confirm that our method preserves realistic garment dynamics under challenging poses and unseen clothing structures.

Background The annotation of fashion images is a significantly important task in the fashion industry as well as social media and e-commerce. However, owing to the complexity and diversity of fashion images, this task entails multiple challenges, including the lack of fine-grained captions and confounders caused by dataset bias. Specifically, confounders often cause models to learn spurious correlations, thereby reducing their generalization capabilities. Method In this work, we propose the Deconfounded Fashion Image Captioning (DFIC) framework, which first uses multimodal retrieval to enrich the predicted captions of clothing, and then constructs a detailed causal graph using causal inference in the decoder to perform deconfounding. Multimodal retrieval is used to obtain semantic words related to image features, which are input into the decoder as prompt words to enrich sentence descriptions. In the decoder, causal inference is applied to disentangle visual and semantic features while concurrently eliminating visual and language confounding. Results Overall, our method can not only effectively enrich the captions of target images, but also greatly reduce confounders caused by the dataset. To verify the effectiveness of the proposed framework, the model was experimentally verified using the FACAD dataset.

Virtual garment animation and deformation constitute a pivotal research direction in computer graphics, finding extensive applications in domains such as computer games, animation, and film. Traditional physics-based methods can simulate the physical characteristics of garments, such as elasticity and gravity, to generate realistic deformation effects. However, the computational complexity of such methods hinders real-time animation generation. Data-driven approaches, on the other hand, learn from existing garment deformation data, enabling rapid animation generation. Nevertheless, animations produced using this approach often lack realism, struggling to capture subtle variations in garment behavior. We proposes an approach that balances realism and speed, by considering both spatial and temporal dimensions, we leverage real-world videos to capture human motion and garment deformation, thereby producing more realistic animation effects. We address the complexity of spatiotemporal attention by aligning input features and calculating spatiotemporal attention at each spatial position in a batch-wise manner. For garment deformation, garment segmentation techniques are employed to extract garment templates from videos. Subsequently, leveraging our designed Transformer-based temporal framework, we capture the correlation between garment deformation and human body shape features, as well as frame-level dependencies. Furthermore, we utilize a feature fusion strategy to merge shape and motion features, addressing penetration issues between clothing and the human body through post-processing, thus generating collision-free garment deformation sequences. Qualitative and quantitative experiments demonstrate the superiority of our approach over existing methods, efficiently producing temporally coherent and realistic dynamic garment deformations. • Both spatial and temporal dimensions are considered in terms of human movement. • Feature parameter fusion strategy to integrate human shape and motion features. • The attentional mechanism establishes dependencies between garment frames. • We resolve the garment-body interpenetration issue through post-processing.

Abstract Specular highlights detection and removal is a challenging task. Although various methods exist for removing specular highlights, they often fail to effectively preserve the color and texture details of objects after highlight removal due to the high brightness and nonuniform distribution characteristics of highlights. Furthermore, when processing scenes with complex highlight properties, existing methods frequently encounter performance bottlenecks, which restrict their applicability. Therefore, we introduce a highlight mask‐guided adaptive residual network (HMGARN). HMGARN comprises three main components: detection‐net, adaptive‐removal network (AR‐Net), and reconstruct‐net. Specifically, detection‐net can accurately predict highlight mask from a single RGB image. The predicted highlight mask is then inputted into the AR‐Net, which adaptively guides the model to remove specular highlights and estimate an image without specular highlights. Subsequently, reconstruct‐net is used to progressively refine this result, remove any residual specular highlights, and construct the final high‐quality image without specular highlights. We evaluated our method on the public dataset (SHIQ) and confirmed its superiority through comparative experimental results.

Due to the limitation of the working principle of 3D scanning equipment, the point cloud obtained by 3D scanning is usually sparse and unevenly distributed. In this paper, we propose a new Generative Adversarial Network(GAN) for point cloud upsampling, which is extended from PU-GAN. Its core architecture is to replace the traditional Self-Attention (SA) module with the implicit Laplacian Off-Set Attention(OA) module, and adjacency features are aggregated using the Multi-Scale Off-Set Attention(MSOA) module, which adaptively adjusts the receptive field to learn various structural features. Finally, Residual links were added to form our Residual Multi-Scale Off-Set Attention (RMSOA) module, which utilized multi-scale structural relationships to generate finer details. A large number of experiments show that the performance of our method is superior to the existing methods, and our model has high robustness.

Image-based virtual try-on, aiming to fit new in-shop clothes into a person image, has gained extensive attention in the fields of computer vision and image process community. However, the existing methods are difficult to generate photo-realistic try-on images when large-scale deformations or large occlusions occur. To address this issue, we propose a novel two stage visual try-on network. Specifically, in the first stage, we used a shape matching model to learn the geometric transformation of in-shop clothes. For the second stage, an U-net with cascaded attention mechanism is presented to learn the composition mask which adjust the clothes and rendered persons. The adjusted clothes and the rendered person are combined by the composition mask to get the final try-on result. Experimental results have shown that our method can generate photo-realistic images with no occlusion.