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Projects / Programmes source: ARIS

Deep generative models for beauty and fashion (DeepBeauty)

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Research activity

Code Science Field Subfield
2.06.02  Engineering sciences and technologies  Systems and cybernetics  System theory and control systems 

Code Science Field
2.02  Engineering and Technology  Electrical engineering, Electronic engineering, Information engineering 
Keywords
computer vision, deep learning, artificial intelligence, image synthesis
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (17)
no. Code Name and surname Research area Role Period No. of publications
1.  11805  PhD Simon Dobrišek  Computer science and informatics  Researcher  2020 - 2024  0 
2.  53820  PhD Žiga Emeršič  Computer science and informatics  Researcher  2020 - 2023  0 
3.  55069  Benjamin Fele  Computer science and informatics  Researcher  2020 - 2021  0 
4.  38118  PhD Klemen Grm  Systems and cybernetics  Researcher  2020 - 2024  0 
5.  53879  Marija Ivanovska  Systems and cybernetics  Researcher  2020 - 2024  0 
6.  35689  PhD Jaka Kravanja  Computer science and informatics  Researcher  2020 - 2024  0 
7.  31985  PhD Janez Križaj  Systems and cybernetics  Researcher  2020 - 2024  0 
8.  55070  Ajda Lampe  Computer science and informatics  Researcher  2022 - 2024  0 
9.  53819  Blaž Meden  Computer science and informatics  Researcher  2020 - 2024  0 
10.  19226  PhD Peter Peer  Computer science and informatics  Researcher  2020 - 2024  0 
11.  51910  Martin Pernuš  Computer science and informatics  Junior researcher  2020 - 2022  0 
12.  21310  PhD Janez Perš  Systems and cybernetics  Researcher  2020 - 2024  0 
13.  53724  Peter Rot  Computer science and informatics  Researcher  2020 - 2024  0 
14.  09581  PhD Franc Solina  Computer science and informatics  Researcher  2020 - 2024  0 
15.  28458  PhD Vitomir Štruc  Systems and cybernetics  Head  2020 - 2024  0 
16.  52095  Matej Vitek  Computer science and informatics  Junior researcher  2020 - 2021  0 
17.  12000  PhD Jerneja Žganec Gros  Computer science and informatics  Researcher  2020 - 2024  0 
Organisations (3)
no. Code Research organisation City Registration number No. of publications
1.  1538  University of Ljubljana, Faculty of Electrical Engineering  Ljubljana  1626965  65 
2.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  0 
3.  1986  ALPINEON R & D  Ljubljana  1820931  0 
Abstract
Advances in artificial intelligence (AI) and deep learning have contributed significantly to the development of recent deep generative models, which today are able to generate photo-realistic and visually convincing images of various objects and even complex scenes. Especially for application domains (e.g., face-related applications), where considerable amounts of training data are publicly available, impressive results with stunning visual quality have been achieved and presented in the literature. Furthermore, due to the nature of these generative models, it is not only possible to generate artificial images, but also to modify (or edit) certain appearance characteristics towards a desired goal. Image generation and editing technology has important applications across a wide variety of industries, including autonomous driving, robotics, quality control, manufacturing, design, entertainment, animation, social media and many more. Especially appealing here are human-centered image generation and editing techniques (e.g., face and body generation and editing) for the beauty and fashion industries which enable designing applications that allow users to virtually try on clothes, fashion accessories, makeup or specific hairstyles. Such virtual try-on technology has not only an immense market potential, but may transform the way people shop for beauty items and apparel, while saving costs for retailers. To put the significance of such technology into perspective, it is worth noting that online apparel and accessories sales (without beauty items) are expected to reach 145 billion USD in 2023 up from 96 billion in 2016 in the US alone. While this is driven by the convenience of online shopping, concerns over the appearance of a particular fashion item on the consumers themselves (instead of fashion models) still limits the growth of this sector to a considerable extent. Virtual try-on technology can thus enhance the shopping experience of the consumers and drive new traffic towards e-commerce platforms for both fashion and beauty. Despite the potential market value and the societal and environmental implications, a wide-spread adoption of virtual try-on technology is still hindered by the current state of the technology. Existing products in this area are commonly based on 3D models, three-dimensional body shape modelling and computationally expensive computer graphics, that require specialized hardware and dedicated imaging equipment which consequently limit the deployment possibilities for the technology. Within the fundamental research project Deep generative models for beauty and fashion (DeepBeauty) we will try to address this issue and conduct research on image generation and editing technology with a particular focus on deep learning methodologies, which have recently been shown to be a highly convenient and effective tool for this task. Our goal is to develop novel (flexible and robust) mechanisms for image editing (without explicit 3D modelling) tailored towards the needs of the beauty and fashion industries, capable of altering certain parts of the input images in accordance with predefined target appearances (e.g., an example of specific makeup, an image of a model wearing a fashion item, clothing or accessory). The main tangible result of the project will be novel and highly robust virtual try-on technology based on original approaches to face and body editing. The developed technology will be able to edit images in a photo-realistic manner, while preserving the overall visual appearance of the subjects in the images. Our research will focus on editing techniques applied in the latent space of Generative Adversarial Networks (GANs), which are the de facto standard today for solving generative computer vision problems. The feasibility of the project is ensured by the past performance of the research groups and the extensive experience of the project partners in generative deep models and semantic image editing.
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