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

How to create a deepfake? Why can they be dangerous? – Dr. Dimosthenis Karatzas at “el Periodico” and “Maldita Twitchería”

Following the way launched by the debate “Fake news and Deepfakes: surviving to an invented reality“, organized by the CVC and Fundación “la Caixa, Dr. Dimosthenis Karatzas, deputy director of the Computer Vision Center (CVC), has explained to different media the problematic associated to the deepfakes. Concretely he was interviewed by Michele Catanzaro for “el … Read more

STOP project: detection of suicidal ideation on social media

STOP is a research project that studies mental health issues on social media through Artificial Intelligence to find patterns related to the high risk of suicide or depression. A recently published study in the Journal of Medical Internet Research shows very promising results. Dr. Ana Freire (principal investigator, Universitat Pompeu Fabra, UPF) and her team, among which are our CVC researchers Dr. Jordi González … Read more

“The automobile of the future” – Dr. Antonio López and Rubén Prados at Vallès Visió

Dr. Antonio López, CVC researcher, and Rubén Prados, project manager of the CVC project “Public Transport with Autonomous Vehicles in a Rural Environment”, were interviewed by Alba Castilla at the program Visions (Vallès Visió) to talk about the vehicle and the mobility of the future. The automotive industry is still exploring the possibilities of autonomous … Read more

Los probadores del futuro en los medios de comunicación

Los probadores del futuro, el último trabajo del Dr. Sergio Escalera, líder del grupo de investigación Human Pose Recovery and Behavior Analysis del Centro de Visión por Computador (CVC) – Universidad de Barcelona (UB), sigue de actualidad. En las últimas semanas ha suscitado el interés de medios de comunicación tales cómo la Agencia SINC, Onda … Read more

El Centre de Visió per Computador s’uneix a la carta oberta de l’ACER “Ara més que mai: cal impulsar la ciència i la innovació”

El Dr. Josep Llados, Director del Centre de Visió per Computador (CVC) i membre de la junta de l’Associació Catalana d’Entitats de Recerca (ACER), ha signat la carta oberta “Ara més que mai: cal impulsar la ciència i la innovació” on l’ACER demana al futur nou govern català aprofitar els fons de recuperació de la … 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.


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

New advances in the detection of bias in face recognition algorithms

A team from the Computer Vision Center (CVC) and the University of Barcelona has published the results of a study that evaluates the accuracy and bias in gender and skin colour of automatic face recognition algorithms tested with real world data. Although the top solutions exceed the 99.9% of accuracy, researchers have detected some groups … Read more

The role of the CVC in the fight against Covid-19 and lung cancer

La Vanguardia -in its printed edition- and Univadis Spain, featured the CVC research aimed to develop a digital biopsia that may be used in the diagnosis of Covid-19 and lung cancer. Members of the Interactive Augmented Modelling for Biomedicine (IAM4B) leaded by Dr. Dèbora Gil and Dr. Carles Sánchez, in collaboration with researchers from the … Read more

Time Machine project at Equal Times

The Time Machine project has been mentioned in this article about Big Data and collective memory featured at the digital magazine Equal Times (in English, Spanish and French):