Presentació del DIH4CAT

La Generalitat de Catalunya i una desena d’agents econòmics, centres tecnològics i de coneixement catalans han anunciat aquest dijous la creació del Digital Innovation Hub de Catalunya (DIH4CAT) per accelerar la incorporació de tecnologia avançada per part de les empreses i institucions catalanes. Es tracta d’un consorci publicoprivat impulsat pel Departament d’Empresa i Treball mitjançant … Read more

El discurs racista a Twitter durant la pandèmia s’invisibilitza i es camufla d’informació objectiva

Els nostres investigadors Jordi Gonzalez Sabaté i Diego Velazquez han col·laborat amb Fundació Autònoma Solidària (FAS) i NOVACT a l’estudi “Racisme Digital i Covid-19. Discursos racistes i antiracistes a Twitter durant la pandèmia” amb el suport de Cooperació Catalana Generalitat de Catalunya.  Gràcies al suport de l’Agència Catalana de Cooperació al Desenvolupament (ACCD), el CVC-UAB, … Read more

CVC & Cetaqua Joint Lab

Imagen médica, vehículo autónomo, robótica, entretenimiento… La visión por computador es uno de los ámbitos de la Inteligencia Artificial que está generando un impacto más tangible en nuestras vidas. En este marco, Cetaqua, Centro Tecnológico del Agua de Aigües de Barcelona, la UPC y el CSIC, y el Computer Vision Center (CVC) han firmado un acuerdo de … Read more

New system powered by deep learning makes it possible to detect Covid-19 lesions by analysing CT chest scans

Researchers From The Eurecat Technology Centre, The CVC And The University Of Barcelona Have Developed An Automated System That Taps Into Deep Learning Technology To Detect Lesions Caused By Covid-19 By Reading Computed Tomography (CT) Chest Images. The study, conducted by researchers Giuseppe Pezzano, Vicent Ribas, Petia Radeva and Oliver Díaz, was recently published in the … Read more

Computer Vision meets archaeology to detect near 10k Archaeological Tumuli in Galicia

Researchers From The Computer Vision Center And The Landscape Archaeology Research Group (GIAP) Of The Catalan Institute Of Classical Archaeology (ICAC) Have Developed A Hybrid Algorithm That Combines Deep Learning And Machine Learning To Improve The Automatic Detection Of Archaeological Tumuli Avoiding The Inclusion Of Most False Positives. Archaeological tumuli are one of the most common types of archaeological … Read more

The Computer Vision Center, Eurecat and Universitat de Barcelona, together in the fight against COVID-19

Currently, with the PCR and antigen test, clinicians have really good tools for diagnosing the COVID-19 disease when the SARS-CoV-2 virus is active within the patient organism. However, these tests have two drawbacks: first, when patients suffer pulmonary infection, these tests do not provide precise information regarding its extension, and second, once the virus is … Read more

Radiolung wins the Innovation Award from the Lung Ambition Alliance for lung cancer early detection

Radiolung is aimed to develop an artificial intelligence system based on radiomic information to improve the malignancy detection of lung nodules. The research team is compound by members of the CVC research group Interactive Augmented Modeling for Biomedicine, led by Dr. Dèbora Gil, and various departments of the Hospital and Research Institute Germans Trias i … Read more

Artificial intelligence tool developed to monitor via satellite the destruction of buildings in wars

Researchers from Barcelona and California, led by the Institute of Economic Analysis (IAE-CSIC) and the UAB, and with the participation of the Computer Vision Center (CVC), have applied machine learning to detect the destruction of buildings by artillery using neural networks. This automated method would make possible to monitor the destruction of a bellicose conflict, … Read more

Journey through the history of TV3 thanks to Computer Vision

Can you imagine having a time machine and being able to travel through the history of television? The ViVIM (Computer Vision for Multi-Platform Immersive Video) project, with the participation of the Computer Vision Center (CVC), has developed an immersive tool that allows a unique experience: to travel in a virtual elevator that transport users to … Read more

The fitting rooms of the future

Researchers from the Computer Vision Center (CVC) and the University of Barcelona (UB) have developed, using deep learning, CLOTH3D, the first 3D big scale synthetic dataset for simulating clothes on top of different body shapes. This dataset is the first step to allow virtual enhanced try-ons experience.

Every day, more and more people buy their clothes using virtual platforms, and the current pandemic situation is even speeding up this trend. The advantages of this new way of shopping are evident, but it has some shortcomings too. One of the most important is that people cannot try on the clothes before receiving it at their place. To solve this problem, Artificial Intelligence and deep learning are playing a key role, since they are allowing modelling, recovery and generation of 3D model clothes. These models will mean a breakthrough for enhanced virtual try-ons experience, reducing designer’s and animator’s workload.

Nowadays, it exits models for simulating clothes on top of body shapes, but they are almost focused on 2D. This is because 3D models need an enormous amount of data, and available 3D cloth data are very scarce. There are three main strategies in order to produce data of 3D-dressed humans: 3D scans, 3D from conventional images, and synthetic generation. In the case of 3D scans, they are costly, and at most, they can produce a single mesh (human + garments). Alternatively, datasets that infer 3D geometry of clothes from conventional images are inaccurate and cannot properly model cloth dynamics. Finally, synthetic data is easy to generate and is ground truth error-free.

Researchers from the Human Pose Recovery and Behavior Analysis Group at the Computer Vision Center (CVC) – University of Barcelona (UB) chose this last path and developed CLOTH3D, the first big-scale synthetic dataset of 3D clothed human sequences, which was recently published in the Computer Vision – ECCV 2020 journal.  “As a lot of data is needed for developing 3D models, we decided to generate our own data. We have designed and released the biggest dataset of this kind with a strong outfit variability and rich cloth dynamics”, explained Hugo Bertiche (UB – CVC).

CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. CLOTH3D is unique in terms of garment, shape, and pose variability, including more than 2 million 3D samples. “We developed a generation pipeline that creates a unique outfit for each sequence in terms of garment type, topology, shape, size, tightness and fabric. While other datasets contain just a few different garments, ours is the biggest data set in this field nowadays, with thousands of different garments. But we did not focus only on its development, we also published it in open access, so it is available for all types of audiences”, Dr. Sergio Escalera (CVC-UB) pointed out.

But cloth manufacturing industry is not the only one that could take advantage of this dataset, “the entertainment industry could also benefit, since computer-generated image movies and videogames could be even more realistic”, argue Dr. Meysam Madadi (CVC). But there is still plenty of work to do, “understanding 3D garments through deep learning is still in early stages. On one hand, while our dataset covers most day-to-day garment variability, outfit styles are only limited by imagination. Faster, automatic and smart garment design could lead to many very interesting applications. On the other hand, cloth dynamics are extremely complex and challenging, and they have been barely tackled in very naive ways. Further exploring is a must for this community. Finally, real fabrics are much more than what simulators usually provide, deep learning has yet to find the proper way to model extremely fine and chaotic details such as wrinkles and also objects of arbitrary geometry related to outfits, such as hats, glasses, gloves, shoes, trinkets and more”, concluded H. Bertiche.

Reference:

Bertiche H., Madadi M., Escalera S. (2020) CLOTH3D: Clothed 3D Humans. Computer Vision – ECCV 2020. Lecture Notes in Computer Science, vol 12540. Springer, Cham. DOI: 10.1007/978-3-030-58565-5_21