Warping Process Research Articles

StyleVTON: A multi-pose virtual try-on with identity and clothing detail preservation

Virtual try-on models have been developed using deep learning techniques to transfer clothing product images onto a candidate. While previous research has primarily focused on enhancing the realism of the garment transfer, such as improving texture quality and preserving details, there is untapped potential to further improve the shopping experience for consumers. The present study outlines the development of an innovative multi-pose virtual try-on model, namely StyleVTON, to potentially enhance consumers’ shopping experiences. Our method synthesises a try-on image while also allowing for changes in pose. To achieve this, StyleVTON first predicts the segmentation of the target pose based on the target garment. Next, the segmentation layout guides the warping process of the target garment. Finally, the pose of the candidate is transferred to the desired posture. Our experiments demonstrate that StyleVTON can generate satisfactory images of candidates wearing the desired clothes in a desired pose, potentially offering a promising solution for enhancing the virtual try-on experience. Our findings reveal that StyleVTON outperforms other comparable methods, particularly in preserving the facial identity of the candidate and geometrically transforming the garments.

Improving texture integrity through second-order constraints on warping maps

Pose Transfer has recently gained significant attention, particularly for its user-friendly applications in the animation industry. The primary objective is to transform a given RGB image into a new target pose. This process involves two consecutive tasks: initially, warping the image to approximately align with the target pose, and subsequently using this rough estimation to generate a photorealistic image of the input in the desired pose.The primary challenge lies in the first task, where the image undergoes a rough transformation to its new location in the target pose. Current deep learning approaches rely on first-order warping, employing an affine transformation to move all image pixels. Despite yielding promising results, this approach has significant challenges when dealing with complex deformations, mainly due to the simplistic nature of its linear function. In contrast, we suggest transferring patches using a set of correlation layers. In each layer, the warping for each pixel of the image is individually estimated. We additionally introduce a constraint aimed at minimizing the energy of second derivatives across the entire warping map of the pixels. This allows for keeping the integration of local textures following the warping process, a feature already ensured in the affine-based transformation by restricting the transition to a linear function for all the image pixels. Our approach not only preserves the integrity of local textures, akin to the affine transformation, but achieves this by individually estimating the warping for each image pixel, thereby enabling finer adjustments of the input sample to the target pose. We illustrate the superior performance of this technique compared to affine-based strategies on the renowned DeepFashion database.

Joint facial action unit recognition and self-supervised optical flow estimation

Facial action unit (AU) recognition and optical flow estimation are two highly correlated tasks, since optical flow can provide motion information of facial muscles to facilitate AU recognition. However, most existing AU recognition methods handle the two tasks independently by offline extracting optical flow as auxiliary information or directly ignoring the use of optical flow. In this paper, we propose a novel end-to-end joint framework of AU recognition and optical flow estimation, in which the two tasks contribute to each other. Moreover, due to the lack of optical flow annotations in AU datasets, we propose to estimate optical flow in a self-supervised manner. To regularize the self-supervised estimation of optical flow, we propose an identical mapping constraint for the optical flow guided image warping process, in which the estimated optical flow between two same images is required to not change the image during warping. Experiments demonstrate that our framework (i) outperforms most of the state-of-the-art AU recognition methods on the challenging BP4D and GFT benchmarks, and (ii) also achieves competitive self-supervised optical flow estimation performance.

Image Morphing Techniques: A Review

Nowadays image morphing has become one of the important techniques in applications that require a graphical representation of objects. Morphing tools have become very well known among users who work on multimedia applications such as art effects, virtual games, photo morphing, and social media, in addition to scientific and academic fields. There are many algorithms to apply morphing operations, including the basic and improved techniques, which share some essential stages, but vary in the algorithm details and the produced image qualities. Morphing techniques, in general, are based on image features and changing them through a warping process to produce another image or mixing two images to produce a new combined image. This paper provides an overview of different morphing techniques explaining how they work and discuss their features in some terms such as the morph visual quality, technical efficiency, and complexity, which can assist the researcher in the image morphing field to compare and identify morphing techniques that suit their working area.

Video Frame Interpolation Based on Symmetric and Asymmetric Motions

Video frame interpolation is the task to synthesize intermediate frames between consecutive frames to increase the frame rate. Recently, various deep-learning techniques have been proposed to interpolate intermediate frames more reliably. However, many existing methods use either symmetric (linear) or asymmetric (non-linear) schemes only to estimate motions for the warping process, resulting in unreliable interpolation results. In this paper, we propose a novel video frame interpolation network based on both symmetric and asymmetric motion-based warping modules, which can deal with linear and non-linear motions, as well as occlusions, effectively. The symmetric warping module estimates symmetric motions to generate intermediate frames, while the asymmetric one predicts asymmetric motions to address non-linear motions and occlusion problems. We combine symmetric and asymmetric warping results to reconstruct intermediate frames more reliably. We also develop the frame synthesis network to refine the combined warping results. Experimental results demonstrate that the proposed network outperforms state-of-the-art video interpolation algorithms and that the two types of warping modules work effectively in a complementary manner on various benchmark datasets.

Forward Warping-Based Video Frame Interpolation Using a Motion Selective Network

Recently, deep neural networks have shown surprising results in solving most of the traditional image processing problems. However, the video frame interpolation field does not show relatively good performance because the receptive field requires a vast spatio-temporal range. To reduce the computational complexity, in most frame interpolation studies, motion is first calculated with the optical flow, then interpolated frames are generated through backward warping. However, while the backward warping process is simple to implement, the interpolated image contains mixed motion and ghosting defects. Therefore, we propose a new network that does not use the backward warping method through the proposed max-min warping. Since max-min warping generates a clear warping image in advance according to the size of the motion and the network is configured to select the warping result according to the warped layer, using the proposed method, it is possible to optimize the computational complexity while selecting a contextually appropriate image. The video interpolation method using the proposed method showed 34.847 PSNR in the Vimeo90k dataset and 0.13 PSNR improvement compared to the Quadratic Video Interpolation method, showing that it is an efficient frame interpolation self-supervised learning.

Vehicle count prediction using machine learning

In this research, we provide a regression-based technique for counting and classifying freeway traffic. This technique does not need the normal division and monitoring of personal vehicles. Numerous pre-existing routines. It is thus important that this solution be used. Significant obstructions or low-resolution vehicles may benefit from this tool low, where the derived characteristics are notoriously unstable. For example, our method has two important contributions. Firstly, the backdrop segments are detected using a stretching approach that has been devised. Unclassified cars are found in these areas. Mathematical models vehicles may be tracked using Kaplan filtering (e.g. it's not necessary to minimize the deformation of the vehicle induced by the mesh grid with a quel motion parallax effect throughout the warping process, transformations are calculated and applied. N process. A subset of these low-level traits is then extracted. Construct a tumbled linear regression for the foreground section to directly number and categorize automobiles, without the aid of any third party pertaining to connected vehicles. There are three distinct ways to approach this. The creation and assessment of regression regressions. Our findings are supported by experiments. With low-quality datasets, linear extrapolation algorithms are accurate and resilient many present techniques fail effectively extract useful information from qualities you can rely on high index terms.

SPG-VTON: Semantic Prediction Guidance for Multi-Pose Virtual Try-on

Image-based virtual try-on is challenging in fitting a target in-shop clothes onto a reference person under diverse human poses. Previous works focus on preserving clothing details ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> texture, logos, patterns) when transferring desired clothes onto a target person under a fixed pose. However, the performances of existing methods significantly dropped when extending existing methods to multi-pose virtual try-on. In this paper, we propose an end-to-end Semantic Prediction Guidance multi-pose Virtual Try-On Network (SPG-VTON), which can fit the desired clothing into a reference person under arbitrary poses. Specifically, SPG-VTON is composed of three sub-modules. First, a Semantic Prediction Module (SPM) generates the desired semantic map. The predicted semantic map provides more abundant guidance to locate the desired clothing region and produce a coarse try-on image. Second, a Clothes Warping Module (CWM) warps in-shop clothes to the desired shape according to the predicted semantic map and the desired pose. Specifically, we introduce a conductible cycle consistency loss to alleviate the misalignment in the clothing warping process. Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose. In addition, we introduce a face identity loss to refine the facial appearance and maintain the identity of the final virtual try-on result at the same time. We evaluate the proposed method on the most massive multi-pose dataset (MPV) and the DeepFashion dataset. The qualitative and quantitative experiments show that SPG-VTON is superior to the state-of-the-art methods and is robust to data noise, including background and accessory changes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , hats and handbags, showing good scalability to the real-world scenario.

청양춘포짜기의 전통기술 재현과 전승의 대안

Chunpo is a woven textile with ramie threads in the weft and silk threads in the warp. Cheongyang Chunpo is registered as Intangible Cultural Heritage of Chungcheongnam-do and the techniques preserved. However, its traditional techniques are no longer conducted for more than 20 years, and its’ traditional technique succession has stopped. Chunpo weaving consists of the silk-related weaving process, the Mosi-related weaving process, and Chunpo’s character. Chunpo weaving’s main character is its yellow starch on silk warp during warping process to make its finish vivid yellow color from gardenia seed dyed starching process. For the revival of Chunpo traditional weaving, reeling silk cocoon techniques and yellow starching process plus traditional loom weaving silks are essential points to achieve. For Mosi threads, Hansan Mosi threads can be used, and for silk reeling techniques, Doosan village, which is silk weaving National Intangible Cultural Heritage can be acquired. Also for traditional loom weaving techniques can be acquired from Doosan village. Through this study and weaving trials from 3 ways, it is proved or favorable to success Cheongyang Chunpo’s traditional weaving techniques to be transmitted afterward. The first trial was in Cheongyang, the second trials were in Doosan silk weaving village, and the researcher did the third. Above all, weaving Chunpo in Doosan village or using Doosan silk weaver’s skill and after transmission from Doosan to Cheongyang would be the best and practical solution for Cheongyang Chunpo traditional technique revival.

Thermal Deformations of Thermoplast during 3D Printing: Warping in the Case of ABS.

This research focuses on thermal deformations of thermoplast during three-dimensional printing. A filament acrylonitrile butadiene styrene was used, and the main focus was put on warping. Twenty-seven cuboids divided in six categories by their length, height, surface area, color, nozzle temperature and bed temperature were printed by Fused Filament Fabrication 3D printer. The whole process was captured by a thermal camera and the movies were used to analyze the temperature distribution during printing. All printouts were measured and scanned with a 3D scanner in order to highlight any abbreviations from the original digital models. The obtained results were used to formulate some general conclusions on the influence of selected parameters on the warping process. Based on the outcomes of the study, a set of guidelines on how to minimalize warping was proposed.

Spatial Warped Gaussian Processes: Estimation and Efficient Field Reconstruction

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we study both the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators, the multi-point Maximum A-Posteriori (MAP) estimators, an efficient class of multi-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators, and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

Исследование процессов устранения коробления авиационных изделий из полимерных композиционных материалов, полученных методом высокотемпературного формования

The article considers issues of identifying the causes and factors that have a significant impact on the occurring and development of the warping process in monolithic products made of polymer — composite materials (PCM) obtained by the method of multidirectional layering of uncured PCM — semi-finished product (prepreg) and subsequent high-temperature molding in an autoclave. Warping is understood as a defect of a PCM product in the form of distortion of its configuration (deformation) under stresses arising during the polymerization of the binder prepreg at a high temperature (1800C) and cooling the product to room temperature. Flat samples made of glass-based prepregs have been researched. The selecting schemes of equilibrium stacking of samples of monolithic panels has been carried out. The influence of the binder content in the prepreg on the deformations in non-equilibrium layouts of the panel samples has been evaluated. It is shown that the most significant factors influencing the occurrence of warpage of monolithic PCM panels are: the weaving pattern of the prepreg reinforcing base, the amount of binder deposited in the prepreg, the direction of stacking monolayers in the panel, the edge effect and the shape of the product surface. It was found that the warpage of the sample occurs along the direction of unbalanced shrinkage of the binder. For balanced stacking, compensation in the direction of binder shrinkage and symmetry with respect to the central layer are necessary; more binder in the prepreg reduces the amount of warpage, but only slightly. The development of constructive and technological recommendations based on the results obtained will lead to a reduction in the terms of development of PCM products.

Nonlinear dynamics of flux compactification

We study the nonlinear evolution of unstable flux compactifications, applying numerical relativity techniques to solve the Einstein equations in D dimensions coupled to a q-form field and positive cosmological constant. We show that initially homogeneous flux compactifications are unstable to dynamically forming warped compactifications. In some cases, we find that the warping process can serve as a toy-model of slow-roll inflation, while in other instances, we find solutions that eventually evolve to a singular state. Analogous to dynamical black hole horizons, we use the geometric properties of marginally trapped surfaces to characterize the lower dimensional vacua in the inhomogeneous and dynamical settings we consider. We find that lower-dimensional vacua with a lower expansion rate are dynamically favoured, and in some cases find spacetimes that undergo a period of accelerated expansion followed by contraction.

Feature compensation based on the normalization of vocal tract length for the improvement of emotion-affected speech recognition

The performance of speech recognition systems trained with neutral utterances degrades significantly when these systems are tested with emotional speech. Since everybody can speak emotionally in the real-world environment, it is necessary to take account of the emotional states of speech in the performance of the automatic speech recognition system. Limited works have been performed in the field of emotion-affected speech recognition and so far, most of the researches have focused on the classification of speech emotions. In this paper, the vocal tract length normalization method is employed to enhance the robustness of the emotion-affected speech recognition system. For this purpose, two structures of the speech recognition system based on hybrids of hidden Markov model with Gaussian mixture model and deep neural network are used. To achieve this goal, frequency warping is applied to the filterbank and/or discrete-cosine transform domain(s) in the feature extraction process of the automatic speech recognition system. The warping process is conducted in a way to normalize the emotional feature components and make them close to their corresponding neutral feature components. The performance of the proposed system is evaluated in neutrally trained/emotionally tested conditions for different speech features and emotional states (i.e., Anger, Disgust, Fear, Happy, and Sad). In this system, frequency warping is employed for different acoustical features. The constructed emotion-affected speech recognition system is based on the Kaldi automatic speech recognition with the Persian emotional speech database and the crowd-sourced emotional multi-modal actors dataset as the input corpora. The experimental simulations reveal that, in general, the warped emotional features result in better performance of the emotion-affected speech recognition system as compared with their unwarped counterparts. Also, it can be seen that the performance of the speech recognition using the deep neural network-hidden Markov model outperforms the system employing the hybrid with the Gaussian mixture model.

Voice Transformation Using Two-Level Dynamic Warping and Neural Networks

Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping algorithm in conjunction with an artificial neural network. An outer warping process which temporally aligns blocks of speech (dynamic time warp, DTW) invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp, DFW). The mapping function produced by inner dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Artifacts arising from this amplitude spectral mapping are reduced by reconstructing phase information. Information obtained by this process is used to train an artificial neural network to produce spectral warping information based on spectral input data. The performance of the speech mapping compared using Mel-Cepstral Distortion (MCD) with previous voice transformation research, and it is shown to perform better than other methods, based on their reported MCD scores.

Effect of titanium alloys elasticity modulus on warping of stamped blanks during cooling

Wide spread in the values of the elasticity modulus of the titanium VT6 alloy and its analogs Ti—6Al—4V, Ti—6Al—4V ELI at room temperature and at elevated temperatures is revealed аs result of the literature sources analysis. The data are ambiguous, the available temperature dependences of the elasticity modulus have very different values starting from the temperature T l 500 °C. Mathematical modeling of the warping process is carried out on the example of figurine-shaped stamped blank of turbine blade using various dependences of the elasticity modulus on temperature. Cases of warping during cooling of stamped blank after cooling-down in stamp with and without cumulative deformation are considered. The difference in the course of thermal deformations during the cooling of the workpiece is obtained using different temperature dependences of the elasticity modulus. The presence of preliminary deformation increases the warping of the workpieces.

Yarn degradation during weaving process and its effect on the mechanical behaviours of 3D warp interlock p-aramid fabric for industrial applications

Three dimensional (3D) warp interlock fabric becomes a promising structure due to its good mechanical performances. However, its complex manufacturing process can cause severe yarn damage and affects its overall final performances. The current study addressed the effects weaving process and warp yarn ratios on the multi-filaments yarn degradations and its mechanical performances while 3D warp interlock fabric manufacturing. Four different 3D warp interlock variants having similar fabric architecture, and yarn densities but different warp yarns interchange ratios were manufactured using 930dTex high-performance multi-filament (Twaron® f1000). The multi-filaments yarns at different weaving processes were tested for their tensile properties. The results show that the average tensile strength of twisted yarns show a decrement of 5.68% as compared to the bobbin yarns. Meanwhile, warping process also showed a 16.11% maximum breaking load reduction as compared to the bobbin yarn. Besides, the tensile strength of binding yarn after weaving process for samples 3D-8/0, 3D-8/4, and 3D-8/8 was reduced by 12.79%, 5.22%, and 14.22% respectively as compared to the yarn after warping process. In conclusion, yarn degradation inside the 3D woven structure was affected not only by the various process parameters but also by the type of fabric architecture made with different warp yarn ratios. These phenomena ultimately bring a great influence both on the yarn and overall mechanical performance of the final products. For this, further studies are planned to investigate the multi-filaments yarn degradation effect on the ballistic performances fibrous material as it is directly linked to the yarn performance.

Tetrahedral mesh deformation with positional constraints

Deforming a tetrahedral mesh to conform to geometry modifications is a useful process in applications. This paper presents a method for tetrahedral mesh deformation driven by displacement of partial vertices of the mesh. The basic techniques behind the method are radial basis function (RBF)-based interpolation and adaptive mesh refinement. The method is realized by a warping process that transforms the mesh via iterative RBF-based interpolation to avoid the inversion of tetrahedra. Adaptive refinement by locally bisecting potentially inverted tetrahedra is also introduced to assure sufficiently large warping stepsizes. The refinement is performed on both the input mesh and the warped meshes concurrently to maintain the consistency of the topology. As a result, the method can effectively produce an inversion-free and topology compatible deformation mesh that satisfies hard positional constraints. Experimental results show the effectiveness of the method.

Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

The latest generation of cars are increasingly equipped with driver assistance systems called ADAS (advanced driver assistance systems) which are able to assist the car driver in different driving scenarios, and in the most advanced automation levels, able to take over driving the car if required due to dangerous situations. Therefore, it is essential to adapt the ADAS specifically to the car-driver’s identity in order to better customize the driving assistance. To this end, algorithms that allow correct recognition of the vehicle driver are fundamental and preparatory. In this context, an algorithm for car-driver identity recognition is proposed which allows, with an accuracy close to 99%, recognition of the driver by means of a properly designed pipeline based on the analysis of the car driver PhotoPlethysmoGraphic (PPG) signal. The proposed approach makes use of deep long short-term memory (LSTM) architecture for learning such PPG signal features needed to discriminate one car driver from another. The extended validation and testing of the proposed system confirm the reliability of the proposed pipeline.

A study on the possibility of improving sulfide ore preparation characteristics using the pneumatic separation process

The size, density of the particles and their shape play a significant role in the effectiveness of the pneumatic separation process. An experiment held using a lab machine revealed that the shape of the particles affects their warping from the air-flow. Research has shown that the airflow produced with the flat or narrow side of the ore which are flat and have a large form factor are more likely to be affected by the warping process. The impact of the ore shapes on the results of separating the pieces in terms of their density during the pneumatic separation process can be decreased by producing a structured feed-through into the air-flow using the flat side or by improving the previously selected ore pieces of a flat shape.