Postdoc at Imperial College London. Research in latent disentanglement, geometric deep learning, computer vision, and computer graphics.
London, England
Joined February 2022
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Simone Foti retweeted
Still very excited to receive the #ECCV2026 Best Paper Award for Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat [circle-group.github.io/resea…], along with @simo_foti, @CanerKorkmaz0 and Stefanos Zafeiriou. See Simone's post below for a detailed break-down.
🔥🏆Heat Kernel Textures (HKTex) received the Best Paper Award at #ECCV2026. 🏆🔥
UV-mapping anchors 3D texturing to the past. Inspired by 3DGS, we rethink textures using heat kernels and NO splatting!
circle-group.github.io/resea…
Simone Foti retweeted
📣 Deadline Extended – LOGML'26
Due to ICLR conference & NeurIPS deadlines, applications are now open till 10 May'26.
🚀 LOGML @ Imperial College London
📅 13–17 July'26
🎓 Talks
🤝 Projects
🧠 Collaboration
🎉 Networking
🔗 Apply: forms.gle/RjxJHCdKVJF41Vvv7
Simone Foti retweeted
Our #CVPR2026 work Differentiable Straightest Geodesics [circle-group.github.io/resea…] provides exponential maps for 3D meshes allowing to trace geodesics or parallel-transport vectors. With it, we release DiGeo, a PyTorch differential geometry package: digeo.readthedocs.io/. 👇
Simone Foti retweeted
Our Geometric Neural Distance Fields will be presented at #CVPR2026:
circle-group.github.io/resea….
@CVPR
We present Neural Riemannian Motion Fields (NRMF) as a promising paradigm for generative modeling of articulated motion. Unlike various diffusion based models, NRMF is useful both in generation and in deployment as a prior across tasks: circle-group.github.io/resea…. #CVPR #AI #CVML👇
Simone Foti retweeted
We present Neural Riemannian Motion Fields (NRMF) as a promising paradigm for generative modeling of articulated motion. Unlike various diffusion based models, NRMF is useful both in generation and in deployment as a prior across tasks: circle-group.github.io/resea…. #CVPR #AI #CVML👇
Simone Foti retweeted
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 @simo_foti, we propose UV3-TeD: arxiv.org/abs/2408.16762. 👇
Simone Foti retweeted
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
