Latent Disentanglement for the Analysis and Generation of Digital Human Shapes

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dc.contributor.authorFoti, Simone
dc.date.accessioned2023-12-18T08:27:20Z
dc.date.available2023-12-18T08:27:20Z
dc.date.issued2023-06-30
dc.description.abstractAnalysing and generating digital human shapes is crucial for a wide variety of applications ranging from movie production to healthcare. The most common approaches for the analysis and generation of digital human shapes involve the creation of statistical shape models. At the heart of these techniques is the definition of a mapping between shapes and a low-dimensional representation. However, making these representations interpretable is still an open challenge. This thesis explores latent disentanglement as a powerful technique to make the latent space of geometric deep learning based statistical shape models more structured and interpretable. In particular, it introduces two novel techniques to disentangle the latent representation of variational autoencoders and generative adversarial networks with respect to the local shape attributes characterising the identity of the generated body and head meshes. This work was inspired by a shape completion framework that was proposed as a viable alternative to intraoperative registration in minimally invasive surgery of the liver. In addition, one of these methods for latent disentanglement was also applied to plastic surgery, where it was shown to improve the diagnosis of craniofacial syndromes and aid surgical planning.en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3543924
dc.language.isoenen_US
dc.titleLatent Disentanglement for the Analysis and Generation of Digital Human Shapesen_US
dc.typeThesisen_US
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