Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step, and empirically show that the proposed method reduces the required number of layers to form fully-transparent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image. \keywords{image inpainting, fashion image understanding, dilated convolutions, partial convolutions
Previous virtual try-on methods usually focus on aligning a clothing item with a person, limiting their ability to exploit the complex pose, shape and skin color of the person, as well as the overall structure of the clothing, which is vital to photo-realistic virtual try-on. To address this potential weakness, we propose a fill in fabrics (FIFA) model, a self-supervised conditional generative adversarial network based framework comprised of a Fabricator and a unified virtual try-on pipeline with a Segmenter, Warper and Fuser. The Fabricator aims to reconstruct the clothing image when provided…
Department of Artificial Intelligence and Software, Kangwon National University（人工智能与软件系，康won国立大学）；Department of Electronic and AI System Engineering, Kangwon National University（电子与人工智能系统工程系，康won国立大学）
Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value). This often leads to artifacts such as color discrepancy and blurriness. Post-processing is usually used to reduce such artifacts, but are expensive and may fail. We propose the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. We further include a mechanism to aut…
This paper introduces a semi-parametric approach to image inpainting for irregular holes. The nonparametric part consists of an external image database. During test time database is used to retrieve a supplementary image, similar to the input masked picture, and utilize it as auxiliary information for the deep neural network. Further, we propose a novel method of generating masks with irregular holes and present public dataset with such masks. Experiments on CelebA-HQ dataset show that our semi-parametric method yields more realistic results than previous approaches, which is confirmed by the …
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；Baidu Inc.（百度公司）；Nankai University（南开大学）
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness. Partial convolution has been suggested to address this issue, but it adopts handcrafted feature re-normalization, and only considers forward mask-updating. In this paper, we present a learnable attention map module for learning feature renormalization and mask-updating in an end-to-end manner, which is effective in adapting to i…
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models
Gordon Center for Medical Imaging, Harvard Medical School and Massachusetts General Hospital（哈佛医学院与麻省总医院戈登医学成像中心）；Boston University（波士顿大学）；Seidenberg School of CSIS, Pace University（佩斯大学塞登伯格计算机科学与信息系统学院）；Ewha Womans University（梨花女子大学）
Malong Technologies（码隆科技）；Shenzhen Malong Artificial Intelligence Research Center（深圳码隆人工智能研究中心）；University of Maryland, College Park（马里兰大学帕克分校）
Visual compatibility is critical for fashion analysis, yet is missing in existing fashion image synthesis systems. In this paper, we propose to explicitly model visual compatibility through fashion image inpainting. To this end, we present Fashion Inpainting Networks (FiNet), a two-stage image-to-image generation framework that is able to perform compatible and diverse inpainting. Disentangling the generation of shape and appearance to ensure photorealistic results, our framework consists of a shape generation network and an appearance generation network. More importantly, for each generation …
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Lab（鹏城实验室）；DAMO Academy at Alibaba Group（阿里巴巴达摩院）
For image inpainting, the convolutional neural networks (CNN) in previous methods often adopt standard convolutional operator, which treats valid pixels and holes indistinguishably. As a result, they are limited in handling irregular holes and tend to produce color-discrepant and blurry inpainting result. Partial convolution (PConv) copes with this issue by conducting masked convolution and feature re-normalization conditioned only on valid pixels, but the mask-updating is handcrafted and independent with image structural information. In this paper, we present an edge-guided learnable bidirect…
Facebook Artificial Intelligence（脸书人工智能）；University of Southern California（南加州大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patients brain to a normal one. Many patient images are associated with irregularly distributed lesions, resulting in further distortion of normal tissue structures and complicating registration's similarity measure. In this work, we follow a multi-step contex…
Image inpainting is a technique of completing missing pixels such as occluded region restoration, distracting objects removal, and facial completion. Among these inpainting tasks, facial completion algorithm performs face inpainting according to the user direction. Existing approaches require delicate and well controlled input by the user, thus it is difficult for an average user to provide the guidance sufficiently accurate for the algorithm to generate desired results. To overcome this limitation, we propose an alternative user-guided inpainting architecture that manipulates facial attribute…
Recent advances in deep learning have shown exciting promise in filling large holes and lead to another orientation for image inpainting. However, existing learning-based methods often create artifacts and fallacious textures because of insufficient cognition understanding. Previous generative networks are limited with single receptive type and give up pooling in consideration of detail sharpness. Human cognition is constant regardless of the target attribute. As multiple receptive fields improve the ability of abstract image characterization and pooling can keep feature invariant, specificall…
University of Ontario Institute of Technology（安大略理工大学）
Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image…
Image inpainting has made significant advances in recent years. However, it is still challenging to recover corrupted images with both vivid textures and reasonable structures. Some specific methods only tackle regular textures while losing holistic structures due to the limited receptive fields of convolutional neural networks (CNNs). On the other hand, attention-based models can learn better long-range dependency for the structure recovery, but they are limited by the heavy computation for inference with large image sizes. To address these issues, we propose to leverage an additional structu…
Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Kitware（Kitware公司）；Nanjing Institute of Software Technology（南京软件技术研究院）
Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks. Previous works usually predict the auxiliary structures (e.g., edges, segmentation and contours) to help fill visually realistic patches in a multi-stage fashion. However, imprecise auxiliary priors may yield biased inpainted results. Besides, it is time-consuming for some methods to be implemented by multiple stages of complex neural networks. To solve this issue, we develop an end-to-end multi-modality guided transformer network, including one inpainting branch and two …
Existing approaches for 3D garment reconstruction either assume a predefined template for the garment geometry (restricting them to fixed clothing styles) or yield vertex colored meshes (lacking high-frequency textural details). Our novel framework co-learns geometric and semantic information of garment surface from the input monocular image for template-free textured 3D garment digitization. More specifically, we propose to extend PeeledHuman representation to predict the pixel-aligned, layered depth and semantic maps to extract 3D garments. The layered representation is further exploited to …
The University of Adelaide（阿德莱德大学）；Australian Center for Robotic Vision（澳大利亚机器人视觉中心）；Monash University（莫纳什大学）
We present ``just-in-time reconstruction" as real-time image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In particular, our goal is to inpaint a sparse map --- obtained from either a monocular visual SLAM system or a sparse sensor --- using a single-view depth prediction network as a virtual depth sensor. We adopt a fairly standard approach to data fusion, to produce a fused depth map by performing inference over a novel fully-connected Conditional Random Field (CRF) which is parameterized by the input depth maps and their pix…
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on different given classes of anatomy. In this work, we introduce a method for generating shape-aware masks for inpainting, which aims at learning the statistical shape prior. We hypothesize that although the variation of masks improves the generalizability of inpainting models, the shape of the masks should follow the topology of the organs of interest. Hence, we prop…
Video streams are utilised to guide minimally-invasive surgery and diagnostic procedures in a wide range of procedures, and many computer assisted techniques have been developed to automatically analyse them. These approaches can provide additional information to the surgeon such as lesion detection, instrument navigation, or anatomy 3D shape modeling. However, the necessary image features to recognise these patterns are not always reliably detected due to the presence of irregular light patterns such as specular highlight reflections. In this paper, we aim at removing specular highlights from…
SenseTime Research（商汤科技研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Tencent AI Lab（腾讯人工智能实验室）；The University of HongKong（香港大学）
Image visual try-on aims at transferring a target clothing image onto a reference person, and has become a hot topic in recent years. Prior arts usually focus on preserving the character of a clothing image (e.g. texture, logo, embroidery) when warping it to arbitrary human pose. However, it remains a big challenge to generate photo-realistic try-on images when large occlusions and human poses are presented in the reference person. To address this issue, we propose a novel visual try-on network, namely Adaptive Content Generating and Preserving Network (ACGPN). In particular, ACGPN first predi…
As 3D scanning devices and depth sensors mature, point clouds have attracted increasing attention as a format for 3D object representation, with applications in various fields such as tele-presence, navigation and heritage reconstruction. However, point clouds usually exhibit holes of missing data, mainly due to the limitation of acquisition techniques and complicated structure. Further, point clouds are defined on irregular non-Euclidean domains, which is challenging to address especially with conventional signal processing tools. Hence, leveraging on recent advances in graph signal processin…
We present Poly-GAN, a novel conditional GAN architecture that is motivated by Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose. Poly-GAN allows conditioning on multiple inputs and is suitable for many tasks, including image alignment, image stitching, and inpainting. Existing methods have a similar pipeline where three different networks are used to first align garments with the human pose, then perform stitching of the aligned garment and finally refine the results. Poly-GAN is the first instance where a common architect…
We adapt image inpainting techniques to impute large, irregular missing regions in urban settings characterized by sparsity, variance in both space and time, and anomalous events. Missing regions in urban data can be caused by sensor or software failures, data quality issues, interference from weather events, incomplete data collection, or varying data use regulations; any missing data can render the entire dataset unusable for downstream applications. To ensure coverage and utility, we adapt computer vision techniques for image inpainting to operate on 3D histograms (2D space + 1D time) commo…
Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images
School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8 1TL, UK aff1 Max-Planck-Institut f\"ur Astronomie, K\"onigstuhl 17, 69117 Heidelberg, Germany aff2 SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD, Groningen, The Netherlands aff3 Instituto de Astrof\' sica de Canarias, V\' a L\'actea, 38205 La Laguna, Tenerife, Spain aff4 Instituto de Astrof\'isica de Canarias (IAC)；Departamento de Astrof\'isica, Universidad de La Laguna (ULL), 38200, La Laguna, Tenerife, Spain aff5 Universit\'e PSL, Observatoire de Paris, Sorbonne Universit\'e, CNRS, LERMA, 75014, Paris, France aff6 Universit\'e Paris-Cit\'e, 5 Rue Thomas Mann, 75013, Paris, France aff7 School of Physics, Astronomy；Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK aff8 Aspia Space, Falmouth, TR10 9TA, UK aff9 David A. Dunlap Department of Astronomy \& Astrophysics, University of Toronto, 50 St George Street, Toronto, Ontario M5S 3H4, Canada aff10 Jodrell Bank Centre for Astrophysics, Department of Physics；Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK aff11 Max Planck Institute for Extraterrestrial Physics, Giessenbachstr. 1, 85748 Garching, Germany aff12 Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain aff13 Institut d'Estudis Espacials de Catalunya (IEEC), Edifici RDIT, Campus UPC, 08860 Castelldefels, Barcelona, Spain aff14 Centro de Astrof\' sica da Universidade do Porto, Rua das Estrelas, 4150-762 Porto, Portugal aff15 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal aff16 Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Universit\`a di Bologna, via Piero Gobetti 93/2, 40129 Bologna, Italy aff17 INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, Via Piero Gobetti 93/3, 40129 Bologna, Italy aff18 Department of Mathematics；Physics, Roma Tre University, Via della Vasca Navale 84, 00146 Rome, Italy aff19 INAF-Osservatorio Astronomico di Roma, Via Frascati 33, 00078 Monteporzio Catone, Italy aff20 Universit\'e Paris-Saclay, CNRS, Institut d'astrophysique spatiale, 91405, Orsay, France aff21 ESAC/ESA, Camino Bajo del Castillo, s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Ca\ nada, Madrid, Spain aff22 INAF-Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy aff23 Universit\'e Paris-Saclay, Universit\'e Paris Cit\'e, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France aff24 IFPU, Institute for Fundamental Physics of the Universe, via Beirut 2, 34151 Trieste, Italy aff25 INAF-Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, 34143 Trieste, Italy aff26 INFN, Sezione di Trieste, Via Valerio 2, 34127 Trieste TS, Italy aff27 SISSA, International School for Advanced Studies, Via Bonomea 265, 34136 Trieste TS, Italy aff28 Dipartimento di Fisica e Astronomia, Universit\`a di Bologna, Via Gobetti 93/2, 40129 Bologna, Italy aff29 INFN-Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy aff30 Space Science Data Center, Italian Space Agency, via del Politecnico snc, 00133 Roma, Italy aff31 Dipartimento di Fisica, Universit\`a di Genova, Via Dodecaneso 33, 16146, Genova, Italy aff32 INFN-Sezione di Genova, Via Dodecaneso 33, 16146, Genova, Italy aff33 Department of Physics "E. Pancini", University Federico II, Via Cinthia 6, 80126, Napoli, Italy aff34 INAF-Osservatorio Astronomico di Capodimonte, Via Moiariello 16, 80131 Napoli, Italy aff35 Faculdade de Ci\ encias da Universidade do Porto, Rua do Campo de Alegre, 4150-007 Porto, Portugal aff36 Dipartimento di Fisica, Universit\`a degli Studi di Torino, Via P. Giuria 1, 10125 Torino, Italy aff37 INFN-Sezione di Torino, Via P. Giuria 1, 10125 Torino, Italy aff38 INAF-Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese (TO), Italy aff39 European Space Agency/ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands aff40 Institute Lorentz, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands aff41 Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands aff42 INAF-IASF Milano, Via Alfonso Corti 12, 20133 Milano, Italy aff43 Centro de Investigaciones Energ\'eticas, Medioambientales y Tecnol\'ogicas (CIEMAT), Avenida Complutense 40, 28040 Madrid, Spain aff44 Port d'Informaci\' o Cient\' i fica, Campus UAB, C. Albareda s/n, 08193 Bellaterra (Barcelona), Spain aff45 INFN section of Naples, Via Cinthia 6, 80126, Napoli, Italy aff46 Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA aff47 Dipartimento di Fisica e Astronomia "Augusto Righi" - Alma Mater Studiorum Universit\`a di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy aff48 Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK aff49 European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044 Frascati, Roma, Italy aff50 Universit\'e Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822, Villeurbanne, F-69100, France aff51 Aix-Marseille Universit\'e, CNRS, CNES, LAM, Marseille, France aff52 Institut de Ci\` e ncies del Cosmos (ICCUB), Universitat de Barcelona (IEEC-UB), Mart\' i i Franqu\` e s 1, 08028 Barcelona, Spain aff53 Instituci\'o Catalana de Recerca i Estudis Avan c ats (ICREA), Passeig de Llu\' s Companys 23, 08010 Barcelona, Spain aff54 UCB Lyon 1, CNRS/IN2P3, IUF, IP2I Lyon, 4 rue Enrico Fermi, 69622 Villeurbanne, France aff55 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK aff56 Departamento de F\'isica, Faculdade de Ci\ encias, Universidade de Lisboa, Edif\'icio C8, Campo Grande, PT1749-016 Lisboa, Portugal aff57 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Faculdade de Ci\ encias, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal aff58 Department of Astronomy, University of Geneva, ch. d'Ecogia 16, 1290 Versoix, Switzerland aff59 INFN-Padova, Via Marzolo 8, 35131 Padova, Italy aff60 Aix-Marseille Universit\'e, CNRS/IN2P3, CPPM, Marseille, France aff61 INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere, 100, 00100 Roma, Italy aff62 Universit\"ats-Sternwarte M\"unchen, Fakult\"at f\"ur Physik, Ludwig-Maximilians-Universit\"at M\"unchen, Scheinerstrasse 1, 81679 M\"unchen, Germany aff63 INAF-Osservatorio Astronomico di Padova, Via dell'Osservatorio 5, 35122 Padova, Italy aff64 Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, 0315 Oslo, Norway aff65 Department of Physics, Lancaster University, Lancaster, LA1 4YB, UK aff66 Felix Hormuth Engineering, Goethestr. 17, 69181 Leimen, Germany aff67 Technical University of Denmark, Elektrovej 327, 2800 Kgs. Lyngby, Denmark aff68 Cosmic Dawn Center (DAWN), Denmark aff69 Institut d'Astrophysique de Paris, UMR 7095, CNRS；Sorbonne Universit\'e, 98 bis boulevard Arago, 75014 Paris, France aff70 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA aff71 Department of Physics；Helsinki Institute of Physics, Gustaf H\"allstr\"omin katu 2, 00014 University of Helsinki, Finland aff72 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA aff73 Department of Physics, P.O. Box 64, 00014 University of Helsinki, Finland aff74 Helsinki Institute of Physics, Gustaf H \"a llstr \"o min katu 2, University of Helsinki, Helsinki, Finland aff75 Centre de Calcul de l'IN2P3/CNRS, 21 avenue Pierre de Coubertin 69627 Villeurbanne Cedex, France aff76 Laboratoire d'etude de l'Univers et des phenomenes eXtremes, Observatoire de Paris, Universit\'e PSL, Sorbonne Universit\'e, CNRS, 92190 Meudon, France aff77 SKA Observatory, Jodrell Bank, Lower Withington, Macclesfield, Cheshire SK11 9FT, UK aff78 Dipartimento di Fisica "Aldo Pontremoli", Universit\`a degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy aff79 INFN-Sezione di Milano, Via Celoria 16, 20133 Milano, Italy aff80 Universit\"at Bonn, Argelander-Institut f\"ur Astronomie, Auf dem H\"ugel 71, 53121 Bonn, Germany aff81 INFN-Sezione di Roma, Piazzale Aldo Moro, 2 - c/o Dipartimento di Fisica, Edificio G. Marconi, 00185 Roma, Italy aff82 Department of Physics, Institute for Computational Cosmology, Durham University, South Road, Durham, DH1 3LE, UK aff83 Universit\'e C\ o te d'Azur, Observatoire de la C\ o te d'Azur, CNRS, Laboratoire Lagrange, Bd de l'Observatoire, CS 34229, 06304 Nice cedex 4, France aff84 Universit\'e Paris Cit\'e, CNRS, Astroparticule et Cosmologie, 75013 Paris, France aff85 CNRS-UCB International Research Laboratory, Centre Pierre Binetruy, IRL2007, CPB-IN2P3, Berkeley, USA aff86 University of Applied Sciences；Arts of Northwestern Switzerland, School of Engineering, 5210 Windisch, Switzerland aff87 Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique F\'ed\'erale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland aff88 Aurora Technology for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff89 Institut de F\' i sica d'Altes Energies (IFAE), The Barcelona Institute of Science；Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain aff90 DARK, Niels Bohr Institute, University of Copenhagen, Jagtvej 155, 2200 Copenhagen, Denmark aff91 Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada aff92 Department of Physics；Astronomy, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada aff93 Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada aff94 Centre National d'Etudes Spatiales -- Centre spatial de Toulouse, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France aff95 Institute of Space Science, Str. Atomistilor, nr. 409 M a gurele, Ilfov, 077125, Romania aff96 Consejo Superior de Investigaciones Cientificas, Calle Serrano 117, 28006 Madrid, Spain aff97 Universidad de La Laguna, Departamento de Astrof\' sica, 38206 La Laguna, Tenerife, Spain aff98 Dipartimento di Fisica e Astronomia "G. Galilei", Universit\`a di Padova, Via Marzolo 8, 35131 Padova, Italy aff99 Departamento de F\'isica, FCFM, Universidad de Chile, Blanco Encalada 2008, Santiago, Chile aff100 Universit\"at Innsbruck, Institut f\"ur Astro- und Teilchenphysik, Technikerstr. 25/8, 6020 Innsbruck, Austria aff101 Satlantis, University Science Park, Sede Bld 48940, Leioa-Bilbao, Spain aff102 Centre for Electronic Imaging, Open University, Walton Hall, Milton Keynes, MK7 6AA, UK aff103 Instituto de Astrof\'isica e Ci\ encias do Espa c o, Faculdade de Ci\ encias, Universidade de Lisboa, Tapada da Ajuda, 1349-018 Lisboa, Portugal aff104 Cosmic Dawn Center (DAWN) aff105 Niels Bohr Institute, University of Copenhagen, Jagtvej 128, 2200 Copenhagen, Denmark aff106 Universidad Polit\'ecnica de Cartagena, Departamento de Electr\'onica y Tecnolog\'ia de Computadoras, Plaza del Hospital 1, 30202 Cartagena, Spain aff107 Institut de Recherche en Astrophysique et Plan\'etologie (IRAP), Universit\'e de Toulouse, CNRS, UPS, CNES, 14 Av. Edouard Belin, 31400 Toulouse, France aff108 INFN-Bologna, Via Irnerio 46, 40126 Bologna, Italy aff109 Kapteyn Astronomical Institute, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands aff110 Infrared Processing；Analysis Center, California Institute of Technology, Pasadena, CA 91125, USA aff111 Dipartimento di Fisica e Scienze della Terra, Universit\`a degli Studi di Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy aff112 Istituto Nazionale di Fisica Nucleare, Sezione di Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy aff113 INAF, Istituto di Radioastronomia, Via Piero Gobetti 101, 40129 Bologna, Italy aff114 Department of Physics, Oxford University, Keble Road, Oxford OX1 3RH, UK aff115 INAF - Osservatorio Astronomico di Brera, via Emilio Bianchi 46, 23807 Merate, Italy aff116 INAF-Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano, Italy；INFN-Sezione di Genova, Via Dodecaneso 33, 16146, Genova, Italy aff117 Institut d'Astrophysique de Paris, 98bis Boulevard Arago, 75014, Paris, France aff118 ICL, Junia, Universit\'e Catholique de Lille, LITL, 59000 Lille, France aff119 Instituto de F\'isica Te\'orica UAM-CSIC, Campus de Cantoblanco, 28049 Madrid, Spain aff120 CERCA/ISO, Department of Physics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA aff121 Technical University of Munich, TUM School of Natural Sciences, Physics Department, James-Franck-Str. 1, 85748 Garching, Germany aff122 Max-Planck-Institut f\"ur Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching, Germany aff123 Laboratoire Univers et Th\'eorie, Observatoire de Paris, Universit\'e PSL, Universit\'e Paris Cit\'e, CNRS, 92190 Meudon, France aff124 Departamento de F \' sica Fundamental. Universidad de Salamanca. Plaza de la Merced s/n. 37008 Salamanca, Spain aff125 Universit\'e de Strasbourg, CNRS, Observatoire astronomique de Strasbourg, UMR 7550, 67000 Strasbourg, France aff126 Center for Data-Driven Discovery, Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan aff127 Dipartimento di Fisica - Sezione di Astronomia, Universit\`a di Trieste, Via Tiepolo 11, 34131 Trieste, Italy aff128 ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing, Via Magnanelli 2, Bologna, Italy aff129 California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, USA aff130 Departamento F\'isica Aplicada, Universidad Polit\'ecnica de Cartagena, Campus Muralla del Mar, 30202 Cartagena, Murcia, Spain aff131 Instituto de F\'isica de Cantabria, Edificio Juan Jord\'a, Avenida de los Castros, 39005 Santander, Spain aff132 INFN, Sezione di Lecce, Via per Arnesano, CP-193, 73100, Lecce, Italy aff133 Department of Mathematics；Physics E. De Giorgi, University of Salento, Via per Arnesano, CP-I93, 73100, Lecce, Italy aff134 INAF-Sezione di Lecce, c/o Dipartimento Matematica e Fisica, Via per Arnesano, 73100, Lecce, Italy aff135 CEA Saclay, DFR/IRFU, Service d'Astrophysique, Bat. 709, 91191 Gif-sur-Yvette, France aff136 Institute of Cosmology；Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK aff137 Instituto de Astrof\' sica de Canarias, c/ Via Lactea s/n, La Laguna 38200, Spain. Departamento de Astrof\' sica de la Universidad de La Laguna, Avda. Francisco Sanchez, La Laguna, 38200, Spain aff138 Caltech/IPAC, 1200 E. California Blvd., Pasadena, CA 91125, USA aff139 Ruhr University Bochum, Faculty of Physics；Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing (GCCL), 44780 Bochum, Germany aff140 Department of Physics；Astronomy, Vesilinnantie 5, 20014 University of Turku, Finland aff141 Serco for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff142 ARC Centre of Excellence for Dark Matter Particle Physics, Melbourne, Australia aff143 Centre for Astrophysics \& Supercomputing, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia aff144 Department of Physics；Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa aff145 DAMTP, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK aff146 Kavli Institute for Cosmology Cambridge, Madingley Road, Cambridge, CB3 0HA, UK aff147 Department of Astrophysics, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland aff148 Department of Physics, Centre for Extragalactic Astronomy, Durham University, South Road, Durham, DH1 3LE, UK aff149 Institute for Theoretical Particle Physics；Cosmology (TTK), RWTH Aachen University, 52056 Aachen, Germany aff150 IRFU, CEA, Universit\'e Paris-Saclay 91191 Gif-sur-Yvette Cedex, France aff151 Univ. Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 53, Avenue des Martyrs, 38000, Grenoble, France aff152 INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125, Firenze, Italy aff153 Dipartimento di Fisica, Sapienza Universit\`a di Roma, Piazzale Aldo Moro 2, 00185 Roma, Italy aff154 HE Space for European Space Agency (ESA), Camino bajo del Castillo, s/n, Urbanizacion Villafranca del Castillo, Villanueva de la Ca\ nada, 28692 Madrid, Spain aff155 Theoretical astrophysics, Department of Physics；Astronomy, Uppsala University, Box 515, 751 20 Uppsala, Sweden aff156 Mathematical Institute, University of Leiden, Einsteinweg 55, 2333 CA Leiden, The Netherlands aff157 School of Physics \& Astronomy, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK aff158 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK aff159 Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544, USA aff160 Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, 10010, New York, NY, USA aff161
