Machine Learning Methods for 3D Digitization of Garments and Avatar Interactions
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2024
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Universidad Rey Juan Carlos
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From the moment we are born, interaction with our surroundings is essential for
survival and integration into society. We sense and navigate our world, pick up
objects like toys or food, wear clothes, learn to use tools and communicate with
our peers. As social beings, this is a core part of our existence, so any virtual world
that aims to feel realistic and immersive needs to correctly model and replicate
natural human interaction.
However, this is a challenging task, as human interaction is complex and nu anced. It can be virtualized by animators, but animated avatars are limited to
what has been manually done, and animations are usually full of artistic decisions.
Another way of representing interactions is by capturing them from the real world,
but this process is usually expensive and, while it can produce much more output
than animating, it is still limited to what exists in the data. Physical simulations
are less restricted to the data, as they are driven by mathematical formulations,
but these formulations are difficult to implement and usually very slow to execute,
making them unfit for real time applications.
The recent advancements in machine learning offer a groundbreaking approach
to addressing highly complex problems, such as the virtualization of humans and
human interaction. Instead of modeling and implementing the problem explicitly,
massive amounts of data are fed to the model which can automatically discover and
replicate the complexity and nuance of the problem. By analyzing vast datasets,
machine learning models can automatically capture and replicate the subtleties
of these interactions, avoiding the constraints of traditional methods. This the sis leverages this innovative perspective to explore two pivotal aspects of human interaction: the interplay between humans and garments, and the dynamics of human-to-human interactions.
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Tesis Doctoral leída en la Universidad Rey Juan Carlos de Madrid en 2025.
Directores:
Dan Casas Guix
Marc Comino Trinidad
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