13 citations · 28 across the 4 of their papers we have counts for
4 papers
Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning
Simone Foti, Alexander J. Rickart, Bongjin Koo +9
The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accur…
3D Generative Model Latent Disentanglement via Local Eigenprojection
Simone Foti, Bongjin Koo, Danail Stoyanov +1
Designing realistic digital humans is extremely complex. Most data-driven generative models used to simplify the creation of their underlying geometric shape do not offer control o…
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces
Simone Foti, Bongjin Koo, Danail Stoyanov +1
Learning a disentangled, interpretable, and structured latent representation in 3D generative models of faces and bodies is still an open problem. The problem is particularly acute…
Intraoperative Liver Surface Completion with Graph Convolutional VAE
Simone Foti, Bongjin Koo, Thomas Dowrick +5
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgica…