Modellamento del Corpo Umano in Applicazioni Industriali: Stima delle Pose Multi-Persona e Virtual Try-On per un'Interazione Umana Avanzata

Abstract: Human body modelling is becoming one of the major domains as it serves a wide range of applications, ranging from e-commerce to automation in manufacturing and further research on safety mechanisms. The effective and accurate capture, representation, and interaction of the human body within digital environments is crucial for a wide range of activities, including increasing personalization in consumer experiences and ensuring the efficiency of complex systems in industries. This dissertation will attempt to contribute to a better understanding of human body analysis in two key domains: virtual try-on and multi-person pose estimation. Although the research objectives for both domains are explored separately, they all point to the same goal: to improve the modelling and understanding of human bodies in dynamic environments. The first section deals with virtual try-on technology, which has become increasingly crucial in the online fashion retail industry. Without actually having to wear the items, it enables the customer to see how they would fit and appear on their body. The key contribution of this research work to the field lies in its proposal of a unique high-resolution dataset that addresses significant limitations posed by publicly available datasets at this time. This data includes full-body and multi-category clothing items, with which visual quality can be improved for virtual try-on models. Furthermore, a new method for synthesizing highly detailed try-on images is elaborated, which reflects a person's body shape, pose, and fitting of the garment in a realistic way. This research enhances the authenticity and effectiveness of virtual try-on systems and is a precious contribution to scholars and fashion retailers in improving the online shopping experience. The second part focuses on multi-person pose estimation, a challenging task in which the goal is to detect and predict the body keypoints of multiple individuals within a single image. It has a variety of applications, particularly in the industrial sector, where it may aid in improving workplace safety, human-robot collaboration, and worker performance monitoring. The study presented here introduces a new approach employing a transformer-based architecture, enabling large increases in multi-pose detection accuracy in crowded and complex environments. The proposed method aims at reducing interference between the class prediction and keypoints localization, succeeding in performing even in challenging conditions. This thesis contributes novel insights and tools to improve human body modelling by independently advancing both the state-of-the-art virtual try-on and multi-person pose estimation. These will doubtless have an impact on a wide range of applications and further illustrate the importance of precise human body analysis in both commercial and industrial contexts. Work of this calibre, with careful experimentation and the introduction of new methodologies, forms the ground for further developments within the realm of digital human interaction.

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Fincato, Matteo "Modellamento del Corpo Umano in Applicazioni Industriali: Stima delle Pose Multi-Persona e Virtual Try-On per un'Interazione Umana Avanzata" 2025

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