A great presentation from Simon Foti, PhD in Geometric Deep Learning at UCL! Simon is giving providing an overview of recent self-supervised approaches to train a 3D shape variational autoencoder and encourage a disentangled latent representation of identity attributes #reworkDL

Beyond 3D representations, we must rethink appearance too—textures specifically. UV-maps, the de-facto standard, have inherent flaws: seams, distortions, memory waste, and non-uniform resolution. Can we do better? With Simone Foti, we propose UV3-TeD: arxiv.org/abs/2408.16762. 👇

💡Excited by geometry & ML like our organizing committee?💡 Mentor at LOGML 2025 and help shape projects that can become top publications. It’s academic, social, and seriously fun—Plus, it’s in London 🇬🇧 (July 7-11) and we’ve got you covered. Apply now 👇👇👇

Months of planning, and it's over in a flash! Seeing #LOGML come to life with such a fantastic group was worth every bit of effort. The collaborative spirit during the projects was exactly what we hoped for. Immense gratitude to all who came and made the week a huge success. 👏