Rõivaste tekstureerimine kasutades Kinect V2.0

This thesis describes three new garment retexturing methods for FitsMe virtual fitting room applications using data from Microsoft Kinect II RGB-D camera. The first method, which is introduced, is an automatic technique for garment retexturing using a single RGB-D image and infrared information obtained from Kinect II. First, the garment is segmented out from the image using GrabCut or depth segmentation. Then texture domain coordinates are computed for each pixel belonging to the garment using normalized 3D information. Afterwards, shading is applied to the new colors from the texture image. The second method proposed in this work is about 2D to 3D garment retexturing where a segmented garment of a manikin or person is matched to a new source garment and retextured, resulting in augmented images in which the new source garment is transferred to the manikin or person. The problem is divided into garment boundary matching based on point set registration which uses Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. The final contribution of this thesis is by introducing another novel method which is used for increasing the texture quality of a 3D model of a garment, by using the same Kinect frame sequence which was used in the model creation. Firstly, a structured mesh must be created from the 3D model, therefore the 3D model is wrapped to a base model with defined seams and texture map. Afterwards frames are matched to the newly created model and by process of ray casting the color values of the Kinect frames are mapped to the UV map of the 3D model

Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting

We propose a novel self-supervised framework for retargeting non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and poses, enabling 3D virtual try-on (VTON). Existing self-supervised 3D retargeting methods only support parametric and canonical garments, which can only be draped over parametric body, e.g. SMPL. To facilitate the non-parametric garments and body, we propose a novel method that introduces Isomap Embedding based correspondences matching between the garment and the human body to get a coarse alignment between the two meshes. We perform neural refinement of the coarse alignment in a self-supervised setting. Further, we leverage a Laplacian detail integration method for preserving the inherent details of the input garment. For evaluating our 3D non-parametric garment retargeting framework, we propose a dataset of 255 real-world garments with realistic noise and topological deformations. The dataset contains $44$ unique garments worn by 15 different subjects in 5 distinctive poses, captured using a multi-view RGBD capture setup. We show superior retargeting quality on non-parametric garments and human avatars over existing state-of-the-art methods, acting as the first-ever baseline on the proposed dataset for non-parametric 3D garment retargeting

Virtuaalse proovikabiini 3D kehakujude ja roboti juhtimisalgoritmide uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVirtuaalne riiete proovimine on üks põhilistest teenustest, mille pakkumine võib suurendada rõivapoodide edukust, sest tänu sellele lahendusele väheneb füüsilise töö vajadus proovimise faasis ning riiete proovimine muutub kasutaja jaoks mugavamaks. Samas pole enamikel varem välja pakutud masinnägemise ja graafika meetoditel õnnestunud inimkeha realistlik modelleerimine, eriti terve keha 3D modelleerimine, mis vajab suurt kogust andmeid ja palju arvutuslikku ressurssi. Varasemad katsed on ebaõnnestunud põhiliselt seetõttu, et ei ole suudetud korralikult arvesse võtta samaaegseid muutusi keha pinnal. Lisaks pole varasemad meetodid enamasti suutnud kujutiste liikumisi realistlikult reaalajas visualiseerida. Käesolev projekt kavatseb kõrvaldada eelmainitud puudused nii, et rahuldada virtuaalse proovikabiini vajadusi. Välja pakutud meetod seisneb nii kasutaja keha kui ka riiete skaneerimises, analüüsimises, modelleerimises, mõõtmete arvutamises, orientiiride paigutamises, mannekeenidelt võetud 3D visuaalsete andmete segmenteerimises ning riiete mudeli paigutamises ja visualiseerimises kasutaja kehal. Selle projekti käigus koguti visuaalseid andmeid kasutades 3D laserskannerit ja Kinecti optilist kaamerat ning koostati nendest andmebaas. Neid andmeid kasutati välja töötatud algoritmide testimiseks, mis peamiselt tegelevad riiete realistliku visuaalse kujutamisega inimkehal ja suuruse pakkumise süsteemi täiendamisega virtuaalse proovikabiini kontekstis.Virtual fitting constitutes a fundamental element of the developments expected to rise the commercial prosperity of online garment retailers to a new level, as it is expected to reduce the load of the manual labor and physical efforts required. Nevertheless, most of the previously proposed computer vision and graphics methods have failed to accurately and realistically model the human body, especially, when it comes to the 3D modeling of the whole human body. The failure is largely related to the huge data and calculations required, which in reality is caused mainly by inability to properly account for the simultaneous variations in the body surface. In addition, most of the foregoing techniques cannot render realistic movement representations in real-time. This project intends to overcome the aforementioned shortcomings so as to satisfy the requirements of a virtual fitting room. The proposed methodology consists in scanning and performing some specific analyses of both the user's body and the prospective garment to be virtually fitted, modeling, extracting measurements and assigning reference points on them, and segmenting the 3D visual data imported from the mannequins. Finally, superimposing, adopting and depicting the resulting garment model on the user's body. The project is intended to gather sufficient amounts of visual data using a 3D laser scanner and the Kinect optical camera, to manage it in form of a usable database, in order to experimentally implement the algorithms devised. The latter will provide a realistic visual representation of the garment on the body, and enhance the size-advisor system in the context of the virtual fitting room under study

Autonomous clothes manipulation using a hierarchical vision architecture

This paper presents a novel robot vision architecture for perceiving generic 3-D clothes configurations. Our architecture is hierarchically structured, starting from low-level curvature features to mid-level geometric shapes and topology descriptions, and finally, high-level semantic surface descriptions. We demonstrate our robot vision architecture in a customized dual-arm industrial robot with our inhouse developed stereo vision system, carrying out autonomous grasping and dual-arm flattening. The experimental results show the effectiveness of the proposed dual-arm flattening using the stereo vision system compared with the single-arm flattening using the widely cited Kinect-like sensor as the baseline. In addition, the proposed grasping approach achieves satisfactory performance when grasping various kind of garments, verifying the capability of the proposed visual perception architecture to be adapted to more than one clothing manipulation tasks

Learning RGB-D descriptors of garment parts for informed robot grasping

Robotic handling of textile objects in household environments is an emerging application that has recently received considerable attention thanks to the development of domestic robots. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields a desired configuration. In this work we propose a vision-based method, built on the Bag of Visual Words approach, that combines appearance and 3D information to detect parts suitable for grasping in clothes, even when they are highly wrinkled. We also contribute a new, annotated, garment part dataset that can be used for benchmarking classification, part detection, and segmentation algorithms. The dataset is used to evaluate our approach and several state-of-the-art 3D descriptors for the task of garment part detection. Results indicate that appearance is a reliable source of information, but that augmenting it with 3D information can help the method perform better with new clothing items.This research is partially funded by the Spanish Ministry of Science and Innovation under Project PAU+ DPI2011-2751, the EU Project IntellAct FP7-ICT2009-6-269959 and the ERA-Net Chistera Project ViSen PCIN-2013-047. A. Ramisa worked under the JAE-Doc grant from CSIC and FSE.Peer Reviewe

Multi-frame scene-flow estimation using a patch model and smooth motion prior

This paper addresses the problem of estimating the dense 3D motion of a scene over several frames using a set of calibrated cameras. Most current 3D motion estimation techniques are limited to estimating the motion over a single frame, unless a strong prior model of the scene (such as a skeleton) is introduced. Estimating the 3D motion of a general scene is difficult due to untextured surfaces, complex movements and occlusions. In this paper, we show that it is possible to track the surfaces of a scene over several frames, by introducing an effective prior on the scene motion. Experimental results show that the proposed method estimates the dense scene-flow over multiple frames, without the need for multiple-view reconstructions at every frame. Furthermore, the accuracy of the proposed method is demonstrated by comparing the estimated motion against a ground truth

Automatic tailoring and cloth modelling for animation characters.

The construction of realistic characters has become increasingly important to the production of blockbuster films, TV series and computer games. The outfit of character plays an important role in the application of virtual characters. It is one of the key elements reflects the personality of character. Virtual clothing refers to the process that constructs outfits for virtual characters, and currently, it is widely used in mainly two areas, fashion industry and computer animation. In fashion industry, virtual clothing technology is an effective tool which creates, edits and pre-visualises cloth design patterns efficiently. However, using this method requires lots of tailoring expertises. In computer animation, geometric modelling methods are widely used for cloth modelling due to their simplicity and intuitiveness. However, because of the shortage of tailoring knowledge among animation artists, current existing cloth design patterns can not be used directly by animation artists, and the appearance of cloth depends heavily on the skill of artists. Moreover, geometric modelling methods requires lots of manual operations. This tediousness is worsen by modelling same style cloth for different characters with different body shapes and proportions. This thesis addresses this problem and presents a new virtual clothing method which includes automatic character measuring, automatic cloth pattern adjustment, and cloth patterns assembling. There are two main contributions in this research. Firstly, a geodesic curvature flow based geodesic computation scheme is presented for acquiring length measurements from character. Due to the fast growing demand on usage of high resolution character model in animation production, the increasing number of characters need to be handled simultaneously as well as improving the reusability of 3D model in film production, the efficiency of modelling cloth for multiple high resolution character is very important. In order to improve the efficiency of measuring character for cloth fitting, a fast geodesic algorithm that has linear time complexity with a small bounded error is also presented. Secondly, a cloth pattern adjusting genetic algorithm is developed for automatic cloth fitting and retargeting. For the reason that that body shapes and proportions vary largely in character design, fitting and transferring cloth to a different character is a challenging task. This thesis considers the cloth fitting process as an optimization procedure. It optimizes both the shape and size of each cloth pattern automatically, the integrity, design and size of each cloth pattern are evaluated in order to create 3D cloth for any character with different body shapes and proportions while preserve the original cloth design. By automating the cloth modelling process, it empowers the creativity of animation artists and improves their productivity by allowing them to use a large amount of existing cloth design patterns in fashion industry to create various clothes and to transfer same design cloth to characters with different body shapes and proportions with ease