Occluded Regions Research Articles

GLGNet: light field angular superresolution with arbitrary interpolation rates

Acquiring high-resolution light fields (LFs) is expensive. LF angular superresolution aims to synthesize the required number of views from a given sparse set of spatially high-resolution images. Existing methods struggle with sparsely sampled LFs captured with large baselines. Some methods rely on depth estimation and view reprojection, and are sensitive to textureless and occluded regions. Other non-depth based methods suffer from aliasing or blurring effects due to the large disparity. In addition, most methods require specific models for different interpolation rates, which reduces their flexibility in practice. In this paper, we propose a learning framework that overcomes these challenges by exploiting the global and local structures of LFs. Our framework includes aggregation across both the angular and spatial dimensions to fully exploit the input data and a novel bilateral upsampling module that upsamples each epipolar plane image while better preserving its local parallax structure. Furthermore, our method predicts the weights of the interpolation filters based on both subpixel offset and range difference, allowing angular superresolution at different rates with a single model. We show that our non-depth based method outperforms the state-of-the-art methods in terms of handling large disparities and flexibility on both real-world and synthetic LF images.

Mono‐MVS: textureless‐aware multi‐view stereo assisted by monocular prediction

AbstractThe learning‐based multi‐view stereo (MVS) methods have made remarkable progress in recent years. However, these methods exhibit limited robustness when faced with occlusion, weak or repetitive texture regions in the image. These factors often lead to holes in the final point cloud model due to excessive pixel‐matching errors. To address these challenges, we propose a novel MVS network assisted by monocular prediction for 3D reconstruction. Our approach combines the strengths of both monocular and multi‐view branches, leveraging the internal semantic information extracted from a single image through monocular prediction, along with the strict geometric relationships between multiple images. Moreover, we adopt a coarse‐to‐fine strategy to gradually reduce the number of assumed depth planes and minimise the interval between them as the resolution of the input images increases during the network iteration. This strategy can achieve a balance between the computational resource consumption and the effectiveness of the model. Experiments on the DTU, Tanks and Temples, and BlendedMVS datasets demonstrate that our method achieves outstanding results, particularly in textureless regions.

Inhibition Localized Corrosion of N80 Petroleum Pipeline Steel in NaCl-Na2S Solution Using an Imidazoline Quaternary Ammonium Salt

In this paper, the local corrosion inhibition effect of imidazoline on N80 oil pipeline steel in a NaCl-Na2S solution was studied by the simulated blocking tank cell method, and the corrosion processes of the cathode and anode in the blocking zone were simulated. The blocking corrosion behavior of the pipeline tubing steel N80 in simulated corrosion solutions without and with different concentrations of an imidazoline corrosion inhibitor was studied by chemical analysis and electrochemical analysis. The results show that in the three solution systems, after the anode polarization of the occluded cell, the solution in the occluded region is acidified, the pH value decreases sharply, the migration of Cl− and S2− increases, and the concentration is increased in the blocked area. After adding the imidazoline corrosion inhibitor, the imidazoline inhibitor can reduce the migration of small-radius anions (Cl− and S2−) to the occluded area, inhibit the acidification of the solution in the occluded area, and prevent the dissolution of metals in the occluded area. As a result, the corrosion of the occluded area is slowed down due to the change in the chemical and electrochemical state of the occluded area. In the three corrosion solution systems of 2% Na2S + 5% NaCl, 2% Na2S + 8% NaCl, and 2% Na2S + 10% NaCl, the imidazoline corrosion inhibitor can form an adsorption film on the metal surface, thereby increasing the polarization resistance and decreasing the corrosion rate. The addition of an imidazoline corrosion inhibitor can significantly increase the kinetic constant of anode polarization, which can effectively inhibit the local corrosion of N80 steel in these corrosion systems.

Mnemonic But Not Contextual Feedback Signals Defy Dedifferentiation in the Aging Early Visual Cortex.

Perception is an intricate interplay between feedforward visual input and internally generated feedback signals that comprise concurrent contextual and time-distant mnemonic (episodic and semantic) information. Yet, an unresolved question is how the composition of feedback signals changes across the lifespan and to what extent feedback signals undergo age-related dedifferentiation, that is, a decline in neural specificity. Previous research on this topic has focused on feedforward perceptual representation and episodic memory reinstatement, suggesting reduced fidelity of neural representations at the item and category levels. In this fMRI study, we combined an occlusion paradigm that filters feedforward input to the visual cortex and multivariate analysis techniques to investigate the information content in cortical feedback, focusing on age-related differences in its composition. We further asked to what extent differentiation in feedback signals (in the occluded region) is correlated to differentiation in feedforward signals. Comparing younger (18-30 years) and older female and male adults (65-75 years), we found that contextual but not mnemonic feedback was prone to age-related dedifferentiation. Semantic feedback signals were even better differentiated in older adults, highlighting the growing importance of generalized knowledge across ages. We also found that differentiation in feedforward signals was correlated with differentiation in episodic but not semantic feedback signals. Our results provide evidence for age-related adjustments in the composition of feedback signals and underscore the importance of examining dedifferentiation in aging for both feedforward and feedback processing.

Focus and imagine: Occlusion suppression and repairing transformer for occluded person re-identification

Occlusion scenarios pose a great challenge to person re-identification (ReID) task because various occlusions may weaken the discriminative features and introduce interference. Recently, Transformer-based networks, which can aggregate features of all the image patches to construct global features adaptively, have shown advantages in occluded person ReID. Existing methods mainly adopted Transformer as a feature extractor and enhanced local features from the output of the Transformer encoder. However, during the processing of self-attention blocks, disturbing features from occlusions may be diffused into all the tokens, making it difficult to construct effective local features. Therefore, we consider predicting the occlusion situation of images before feature extraction and guiding the Transformer encoder to focus on visible regions, suppressing interference from occlusion. Furthermore, we propose to imagine the partial target under occlusion and reconstruct pseudo-holistic features for more robust retrieval. To this end, the Occlusion Suppression and Repairing Transformer (OSRTrans) is proposed. First, we use a self-supervised occlusion predictor to predict occlusion scores of image patches. Then the Occlusion Suppression Encoder (OSE), guided by occlusion predictions, suppresses the interference from occlusion regions and constructs a global feature. Finally, inspired by contrastive learning, the Feature Repairing Head (FRH) is proposed to reconstruct pseudo-holistic features. Our method enhances model’s ability of extracting discriminative local features, and achieve the state-of-the-art performance on occluded person ReID benchmarks, e.g., Rank-1 of 72.9% on Occluded-DukeMTMC.

Dual-stream pyramid and attention network for stereo matching

In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet.

Performance analysis of multiview video compression based on MIV and VVC multilayer

AbstractTo represent immersive media providing six degree‐of‐freedom experience, moving picture experts group (MPEG) immersive video (MIV) was developed to compress multiview videos. Meanwhile, the state‐of‐the‐art versatile video coding (VVC) also supports multilayer (ML) functionality, enabling the coding of multiview videos. In this study, we designed experimental conditions to assess the performance of these two state‐of‐the‐art standards in terms of objective and subjective quality. We observe that their performances are highly dependent on the conditions of the input source, such as the camera arrangement and the ratio of input views to all views. VVC‐ML is efficient when the input source is captured by a planar camera arrangement and many input views are used. Conversely, MIV outperforms VVC‐ML when the camera arrangement is non‐planar and the ratio of input views to all views is low. In terms of the subjective quality of the synthesized view, VVC‐ML causes severe rendering artifacts such as holes when occluded regions exist among the input views, whereas MIV reconstructs the occluded regions correctly but induces rendering artifacts with rectangular shapes at low bitrates.

Cost Function Unrolling in Unsupervised Optical Flow.

Steepest descent algorithms, which are commonly used in deep learning, use the gradient as the descent direction, either as-is or after a direction shift using preconditioning. In many scenarios calculating the gradient is numerically hard due to complex or non-differentiable cost functions, specifically next to singular points. This has been commonly overcome by increased DNN model sizes and complexity. In this work we propose a novel mechanism we refer to as Cost Unrolling, for improving the ability of a given DNN model to solve a complex cost function, without modifying its architecture or increasing computational complexity. We focus on the derivation of the Total Variation (TV) smoothness constraint commonly used in unsupervised cost functions. We introduce an iterative differentiable alternative to the TV smoothness constraint, which is demonstrated to produce more stable gradients during training, enable faster convergence and improve the predictions of a given DNN model. We test our method in several tasks, including image denoising and unsupervised optical flow. Replacing the TV smoothness constraint with our loss during DNN training, we report improved results in all tested scenarios. Specifically, our method improves flows predicted at occluded regions, a crucial task by itself, resulting in sharper motion boundaries.

Abstract TMP112: Von Willebrand Factor (VWF) Modulates Smooth Muscle Cell Proliferation During the Outward Remodeling of Leptomeningeal Collateral Arterioles Following Stroke

Introduction: The leptomeningeal collaterals (LMCs) undergo outward remodeling after stroke to increase rescue flow toward the occluded region. We previously showed increased VWF in remodeled LMCs and others have shown that VWF can act as a smooth muscle cell (SMC) mitogen. Here we test the hypothesis that VWF modulates vascular remodeling of LMCs in the post-stroke brain. Methods: Permanent distal middle cerebral artery occlusion (pdMCAO) was performed in C57BL6 WT mice (cohort 1; 4 mos), aged (cohort 2; 24 mos) or VWF KO and WT male mice (cohort 3; 4 mos). At 3, 7, and 14 days after pdMCAO, the pial vessels were processed for VWF/Ki67/SMA immunofluorescence and morphometric analysis. Lastly, distribution of VWF was evaluated in LMCs from human and pig brains. Results: During the 3-14 days post-stroke, WT mice of cohort 1 demonstrated significantly increased VWF in ipsilateral LMCs as well as increased Ki67 positive cells. In ECs, Ki67-positive cells were 10.68 ± 1.48 and 10.73 ± 1.26 per mm of vessel at 3 and 7 days, respectively. In SMCs, Ki67-positive cells were 0.049 ± 0.22 and 4.546 ± 0.95 per mm vessel at 3 and 7 days, respectively. By 14 days, LMC no longer showed Ki67-positive ECs or SMCs. In aged cohort 2, along with attenuated VWF expression and remodeling, the Ki67-positive ECs and SMCs were significantly decreased in the aged mice compared to young mice (Ki67-positive ECs were reduced by 40% and 50% at 3 and 7 days, respectively; Ki67-positive SMCs were reduced by 75% at 7 days). In cohort 3, Ki67-positive SMCs were absent in the VWF-KOs at 3 days post-stroke. However, by 7 days, Ki67-positive SMCs were equivalent between WT and VWF-KOs (4.8-5.0 per mm vessel). Lastly, the presence of VWF was detected in the smooth muscle layer of LMCs of human and pig brains. Conclusions: Our study revealed increased VWF in the mural layer of remodeled LMCs and both EC and SMC proliferation. The remodeling response, VWF expression, and number of proliferative cells were attenuated in LMCs from aged brains. Knockout of VWF resulted in reduced LMC remodeling and delayed SMC proliferation. Our findings also provide evidence for VWF within the smooth muscle layer of pig and human brain arterioles, supporting the broader potential role of VWF as a molecular player in vascular remodeling.

Dual convolutional network based on hypergraph and multilevel feature fusion for road extraction from high-resolution remote sensing images

ABSTRACT Road extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based on hypergraph and multilevel feature fusion (DHM) for road extraction to address these challenges. The DHM consists of two branch networks (HGNN branch and CNN branch) and a bimodal feature fusion module (BFFM). In the HGNN branch, an algorithm is developed to exploit the shape features of roads and construct hypergraphs on the HRSI. Then, hypergraph neural networks are employed for the first time to capture the long-range context of roads to enhance road connectivity. In the CNN branch, a bottleneck fusion module integrated with an encoder-decoder network structure is built to aggregate multiscale local features. In BFFM, the long-range context from the HGNN branch and the local features from the CNN branch are fused through the designed position converter and enhanced graph reasoning module to achieve the complementary advantages of the dual-branch network. Extensive experiments on three datasets show that DHM outperforms other state-of-the-art methods, especially on the GS-Mountain road dataset. Furthermore, DHM significantly improves road extraction in occluded and similar road regions.

Light Field Depth Estimation via Stitched Epipolar Plane Images.

Depth estimation is a fundamental problem in light field processing. Epipolar-plane image (EPI)-based methods often encounter challenges such as low accuracy in slope computation due to discretization errors and limited angular resolution. Besides, existing methods perform well in most regions but struggle to produce sharp edges in occluded regions and resolve ambiguities in texture-less regions. To address these issues, we propose the concept of stitched-EPI (SEPI) to enhance slope computation. SEPI achieves this by shifting and concatenating lines from different EPIs that correspond to the same 3D point. Moreover, we introduce the half-SEPI algorithm, which focuses exclusively on the non-occluded portion of lines to handle occlusion. Additionally, we present a depth propagation strategy aimed at improving depth estimation in texture-less regions. This strategy involves determining the depth of such regions by progressing from the edges towards the interior, prioritizing accurate regions over coarse regions. Through extensive experimental evaluations and ablation studies, we validate the effectiveness of our proposed method. The results demonstrate its superior ability to generate more accurate and robust depth maps across all regions compared to state-of-the-art methods.

Astrocyte-derived exosomal miR-378a-5p mitigates cerebral ischemic neuroinflammation by modulating NLRP3-mediated pyroptosis.

This study aimed to investigate the regulatory role of astrocyte-derived exosomes and their microRNAs (miRNAs) in modulating neuronal pyroptosis during cerebral ischemia. Astrocyte-derived exosomes were studied for treating cerebral ischemia in both in vitro and in vivo models. The effects of astrocyte-derived exosomes on neuroinflammation were investigated by analyzing exosome uptake, nerve damage, and pyroptosis protein expression. High throughput sequencing was used to identify astrocyte-derived exosomal miRNAs linked to pyroptosis, followed by validation via qRT‒PCR. The relationship between these miRNAs and NLRP3 was studied using a dual luciferase reporter assay. This study used miR-378a-5p overexpression and knockdown to manipulate OGD injury in nerve cells. The impact of astrocyte-derived exosomal miR-378a-5p on the regulation of cerebral ischemic neuroinflammation was assessed through analysis of nerve injury and pyroptosis protein expression. Our findings demonstrated that astrocyte-derived exosomes were internalized by neurons both in vitro and in vivo. Additionally, Astrocyte-derived exosomes displayed a neuroprotective effect against OGD-induced neuronal injury and brain injury in the ischemic cortical region of middle cerebral artery occlusion (MCAO) rats while also reducing pyroptosis. Further investigations revealed the involvement of astrocyte-derived exosomal miR-378a-5p in regulating pyroptosis by inhibiting NLRP3. The overexpression of miR-378a-5p mitigated neuronal damage, whereas the knockdown of miR-378a-5p increased NLRP3 expression and exacerbated pyroptosis, thus reversing this neuroprotective effect. Astrocyte-derived exosomal miR-378a-5p has a neuroprotective effect on cerebral ischemia by suppressing neuroinflammation associated with NLRP3-mediated pyroptosis.Further research is required to comprehensively elucidate the signaling pathways by which astrocyte-derived exosomal miR-378a-5p modulates neuronal pyroptosis.

SDMSEAF-YOLOv8: a framework to significantly improve the detection performance of unmanned aerial vehicle images

The detailed, high-resolution images captured by drones pose challenges to target detection algorithms with complex scenes and small-sized targets. Moreover, targets in unmanned aerial vehicle images are usually affected by factors such as viewing perspective, occlusion, and light, which increase the difficulty of target detection. In response to the above issues, we propose an improved SDMSEAF-YOLOv8 for target detection based on YOLOv8, combined with a Bi-directional Feature Pyramid Network, to improve the sensing ability of the model for multiscale targets. A Space-to-depth layer replaces the traditional strided convolution layer to enhance the extraction of fine-grained information and small-sized target features. A Multi-Separated and Enhancement Attention module enhances the feature learning ability of the occluded target region, thus reducing missed and false detections. Four detection heads are employed for tiny target detection, each responsible for different size ranges, so as to improve the accuracy and robustness of small target detection. The conventional non-maximum suppression algorithm is improved, so as to reduce the problem of missed detections under a densely occluded scene by setting the attenuation function to adjust the confidence of the treated box based on the overlap between it and the highest-scoring box. Experiments demonstrate that the accuracy of SDMSEAF-YOLOv8 exceeds that of state-of-the-art models on the VisDrone2019-DET-val dataset, with a mAP of 42.9% at 640-pixel resolution, 14.8% over the baseline YOLOv8-x algorithm model, and 6.0% over the known state-of-the-art Fine-Grained Target Focusing Network model and with twice as fast detection.

ProbIBR: Fast Image-Based Rendering with Learned Probability-Guided Sampling.

We present a general, fast, and practical solution for interpolating novel views of diverse real-world scenes given a sparse set of nearby views. Existing generic novel view synthesis methods rely on time-consuming scene geometry pre-computation or redundant sampling of the entire space for neural volumetric rendering, limiting the overall efficiency. Instead, we incorporate learned MVS priors into the neural volume rendering pipeline while improving the rendering efficiency by reducing sampling points under the guidance of depth probability distributions. Specifically, fewer but important points are sampled under the guidance of depth probability distributions extracted from the learned MVS architecture. Based on the learned probability-guided sampling, we develop a sophisticated neural volume rendering module that effectively integrates source view information with the learned scene structures. We further propose confidence-aware refinement to improve the rendering results in uncertain, occluded, and unreferenced regions. Moreover, we build a four-view camera system for holographic display and provide a real-time version of our framework for free-viewpoint experience, where novel view images of a spatial resolution of 512×512 can be rendered at around 20 fps on a single GTX 3090 GPU. Experiments show that our method achieves 15 to 40 times faster rendering compared to state-of-the-art baselines, with strong generalization capacity and comparable high-quality novel view synthesis performance.

Beyond Photometric Consistency: Geometry-Based Occlusion-Aware Unsupervised Light Field Disparity Estimation.

Although learning-based light field disparity estimation has achieved great progress in the most recent years, the performance of unsupervised light field learning is still hindered by occlusions and noises. By analyzing the overall strategy underlying the unsupervised methodology and the light field geometry implied in epipolar plane images (EPIs), we look beyond the photometric consistency assumption, and design an occlusion-aware unsupervised framework to deal with the situations of photometric consistency conflict. Specifically, we present a geometry-based light field occlusion modeling, which predicts a group of visibility masks and occlusion maps, respectively, by forward warping and backward EPI-line tracing. In order to learn better the noise-and occlusion-invariant representations of the light field, we propose two occlusion-aware unsupervised losses: occlusion-aware SSIM and statistics-based EPI loss. Experiment results demonstrate that our method can improve the estimation accuracy of light field depth over the occluded and noisy regions, and preserve the occlusion boundaries better.

Dual-Pixel Raindrop Removal.

Removing raindrops in images has been addressed as a significant task for various computer vision applications. In this paper, we propose the first method using a dual-pixel(DP) sensor to better address raindrop removal. Our key observation is that raindrops attached to a glass window yield noticeable disparities in DP's left-half and right-half images, while almost no disparity exists for in-focus backgrounds. Therefore, the DP disparities can be utilized for robust raindrop detection. The DP disparities also bring the advantage that the occluded background regions by raindrops are slightly shifted between the left-half and the right-half images. Therefore, fusing the information from the left-half and the right-half images can lead to more accurate background texture recovery. Based on the above motivation, we propose a DP Raindrop Removal Network(DPRRN) consisting of DP raindrop detection and DP fused raindrop removal. To efficiently generate a large amount of training data, we also propose a novel pipeline to add synthetic raindrops to real-world background DP images. Experimental results on constructed synthetic and real-world datasets demonstrate that our DPRRN outperforms existing state-of-the-art methods, especially showing better robustness to real-world situations. Our source codes and datasets will be available at http://www.ok.sc.e.titech.ac.jp/res/SIR/dprrn/dprrn.html.

Scrupulous SCGAN Framework for Recognition of Restored Images with Caffe based PCA Filtration

Computer vision enables to detect many objects in any scenario which helps in various real time application but still face recognition and detection remains a tedious process due to the low resolution, blurriness, noise, diverse pose and expression and occlusions. This proposal develops a novel scrupulous Standardized Convolute Generative Adversarial Network (SCGAN) framework for performing accurate face recognition automatically by restoring the occluded region including blind face restoration. Initially, a scrupulous image refining technique is utilised to offer the appropriate input to the network in the subsequent process. Following the pre-processing stage, a Caffe based Principle Component Analysis (PCA) filtration is conducted which uses convolutional architecture for fast feature embedding that collects spatial information and significant differentiating characteristics to counteract the loss of information existing in pooling operations. Then a filtration method identifies the specific match of the face based on the extracted features, creating uncorrelated variables that optimise variance across time while minimising information loss. To handle all the diversification occurring in the image and accurately recognise the face with occlusion in any part of the face, a novel Standardized Convolute GAN network is used to restore the image and recognise the face using novel Generative Adversarial Network (GAN) networks are modelled. This GAN ensures the normal distribution along with parametric optimization contributing to the high performance with accuracy of 96.05% and Peak Signal to Noise Ratio (PSNR) of 18 and Structural Similarity Index Metric (SSIM) of 98% for restored face recognition. Thus, the performance of the framework based on properly recognizing the face from the generated images is evaluated and discussed.

Scrupulous SCGAN Framework for Recognition of Restored Images with Caffe based PCA Filtration

Computer vision enables to detect many objects in any scenario which helps in various real time application but still face recognition and detection remains a tedious process due to the low resolution, blurriness, noise, diverse pose and expression and occlusions. This proposal develops a novel scrupulous Standardized Convolute Generative Adversarial Network (SCGAN) framework for performing accurate face recognition automatically by restoring the occluded region including blind face restoration. Initially, a scrupulous image refining technique is utilised to offer the appropriate input to the network in the subsequent process. Following the pre-processing stage, a Caffe based Principle Component Analysis (PCA) filtration is conducted which uses convolutional architecture for fast feature embedding that collects spatial information and significant differentiating characteristics to counteract the loss of information existing in pooling operations. Then a filtration method identifies the specific match of the face based on the extracted features, creating uncorrelated variables that optimise variance across time while minimising information loss. To handle all the diversification occurring in the image and accurately recognise the face with occlusion in any part of the face, a novel Standardized Convolute GAN network is used to restore the image and recognise the face using novel Generative Adversarial Network (GAN) networks are modelled. This GAN ensures the normal distribution along with parametric optimization contributing to the high performance with accuracy of 96.05% and Peak Signal to Noise Ratio (PSNR) of 18 and Structural Similarity Index Metric (SSIM) of 98% for restored face recognition. Thus, the performance of the framework based on properly recognizing the face from the generated images is evaluated and discussed.

Pattern Guided UV Recovery for Realistic Video Garment Texturing.

The fast growth of E-Commerce creates a global market worth USD 821 billion for online fashion shopping. What unique about fashion presentation is that, the same design can usually be offered with different cloths textures. However, only real video capturing or manual per-frame editing can be used for virtual showcase on the same design with different textures, both of which are heavily labor intensive. In this paper, we present a pattern-based approach for UV and shading recovery from a captured real video so that the garment's texture can be replaced automatically. The core of our approach is a per-pixel UV regression module via blended-weight multilayer perceptrons (MLPs) driven by the detected discrete correspondences from the cloth pattern. We propose a novel loss on the Jacobian of the UV mapping to create pleasant seams around the folding areas and the boundary of occluded regions while avoiding UV distortion. We also adopts the temporal constraint to ensure consistency and accuracy in UV prediction across adjacent frames. We show that our approach is robust to a variety type of clothes, in the wild illuminations and with challenging motions. We show plausible texture replacement results in our experiment, in which the folding and overlapping of the garment can be greatly preserved. We also show clear qualitative and quantitative improvement compared to the baselines as well. With the one-click setup, we look forward to our approach contributing to the growth of fashion E-commerce.

A materials selection framework for fastener-in-panel geometries using FEM and LEFM, to mitigate coating degradation and environmentally assisted cracking

A finite element method (FEM) model was used to determine the potential, current, and current density distributions for a multi-material fastener/hole-in-plate configuration in an atmospheric environment. The plate consisted of bare Pyrowear 675 with or without a ZnNi coating, and the fastener consisted of Ti-6Al-4V with or without an anodization surface treatment. The potential distribution within the fastener hole was the focus of this work, as this region would represent the location of highest stress within the fastener/panel geometry. Separately, existing literature data were augmented by fracture mechanics-based evaluations of the environment-assisted cracking behavior of Pyrowear 675 as a function of electrode potential Pyrowear 675 in 3.0 M NaCl. Combining the fracture mechanics and FEM results allowed for a unified framework which was able to narrow a material selection matrix of 13 total scenarios to three scenarios, in an atmospheric environment of 0.6 M NaCl, specifically. The delayed drying out of occluded regions after the surface electrolyte has been evaporated was accounted for, with only one of the studied material selection combinations able to reduce the crack susceptibility under those conditions. A single pit within the fastener hole was also considered, and it was determined that the size of the pit had a larger impact on the potential than the location of the pit, which will aid in future computational assumptions.