Latent Disentanglement for the Generation of 3D Digital Humans and Plastic Surgery Applications
The generation of 3D human faces and bodies is a complex task with multiple potential applications ranging from movie and game productions, to augmented and virtual reality, as well as metaverse applications. However, learning a disentangled, interpretable, and structured latent representation in 3D generative models is still an open problem and state-of-the-art methods for latent disentanglement are not able to disentangle identity attributes of faces and bodies. This talk will give an overview of recent self-supervised approaches to train a 3D shape variational autoencoder and encourage a disentangled latent representation of identity attributes. In addition, it will discuss how these methods can improve the diagnosis of craniofacial syndromes and aid surgical planning.
Simone is finishing a PhD at the University College London (UCL). His research lies at the intersection of geometric deep learning, computer vision, and computer graphics and aims at developing new latent disentanglement techniques to improve character generation and shape analysis. During the PhD, he did internships at Disney Research Studios and Adobe Reserch, where he researched single-image 3D face reconstruction methods and implicit functional representations for patch-driven super-resolution of textures.