244 citations · 250 across the 5 of their papers we have counts for
7 papers · 1 filter
Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces
Souhail Hadgi, Luca Moschella, Andrea Santilli +5
Recent works have shown that, when trained at scale, uni-modal 2D vision and text encoders converge to learned features that share remarkable structural properties, despite arising…
Correspondence Learning via Linearly-invariant Embedding
Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi +1
In this paper, we propose a fully differentiable pipeline for estimating accurate dense correspondences between 3D point clouds. The proposed pipeline is an extension and a general…
High-Resolution Augmentation for Automatic Template-Based Matching of Human Models
Riccardo Marin, Simone Melzi, Emanuele Rodolà +1
We propose a new approach for 3D shape matching of deformable human shapes. Our approach is based on the joint adoption of three different tools: an intrinsic spectral matching pip…
Infinite Feature Selection: A Graph-based Feature Filtering Approach
Giorgio Roffo, Simone Melzi, Umberto Castellani +2
We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) r…
Instant recovery of shape from spectrum via latent space connections
Riccardo Marin, Arianna Rampini, Umberto Castellani +3
We introduce the first learning-based method for recovering shapes from Laplacian spectra. Given an auto-encoder, our model takes the form of a cycle-consistent module to map laten…
FARM: Functional Automatic Registration Method for 3D Human Bodies
Riccardo Marin, Simone Melzi, Emanuele Rodolà +1
We introduce a new method for non-rigid registration of 3D human shapes. Our proposed pipeline builds upon a given parametric model of the human, and makes use of the functional ma…