no code implementations • 22 Jan 2024 • Dimitrije Antić, Garvita Tiwari, Batuhan Ozcomlekci, Riccardo Marin, Gerard Pons-Moll
Additionally, we propose CloSe-Net, the first learning-based 3D clothing segmentation model for fine-grained segmentation from colored point clouds.
no code implementations • 21 Dec 2023 • Riccardo Marin, Enric Corona, Gerard Pons-Moll
In this work, we propose two solutions: LoVD, a novel neural field model that predicts the direction towards the localized SMPL vertices on the target surface; and INT, the first self-supervised task dedicated to neural fields that, at test time, refines the backbone, exploiting the target geometry.
no code implementations • ICCV 2023 • Yuxuan Xue, Bharat Lal Bhatnagar, Riccardo Marin, Nikolaos Sarafianos, Yuanlu Xu, Gerard Pons-Moll, Tony Tung
Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining.
1 code implementation • CVPR 2023 • Ilya A. Petrov, Riccardo Marin, Julian Chibane, Gerard Pons-Moll
The intimate entanglement between objects affordances and human poses is of large interest, among others, for behavioural sciences, cognitive psychology, and Computer Vision communities.
3 code implementations • 17 May 2023 • Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodolà
We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference.
1 code implementation • 26 Nov 2022 • Ramana Sundararaman, Riccardo Marin, Emanuele Rodola, Maks Ovsjanikov
In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields.
1 code implementation • 8 Jun 2022 • Donato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà
In this work, we tackle the problem of few-shot graph classification, showing that equipping a simple distance metric learning baseline with a state-of-the-art graph embedder allows to obtain competitive results on the task. While the simplicity of the architecture is enough to outperform more complex ones, it also allows straightforward additions.
no code implementations • 30 May 2022 • Marco Pegoraro, Riccardo Marin, Arianna Rampini, Simone Melzi, Luca Cosmo, Emanuele Rodolà
We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.
no code implementations • 5 May 2022 • Vladimir Guzov, Julian Chibane, Riccardo Marin, Yannan He, Yunus Saracoglu, Torsten Sattler, Gerard Pons-Moll
In order for widespread adoption of such emerging applications, the sensor setup used to capture the interactions needs to be inexpensive and easy-to-use for non-expert users.
1 code implementation • 14 Dec 2021 • Riccardo Marin, Souhaib Attaiki, Simone Melzi, Emanuele Rodolà, Maks Ovsjanikov
In this study, we analyze, for the first time, properties that arise in data-driven learned embedding and their relation to the shape-matching task.
1 code implementation • 4 Aug 2021 • Marco Pegoraro, Simone Melzi, Umberto Castellani, Riccardo Marin, Emanuele Rodolà
In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand.
1 code implementation • NeurIPS 2021 • Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds.
2 code implementations • NeurIPS 2020 • Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov
However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that learning the basis from data can both improve robustness and lead to better accuracy in challenging settings.
Ranked #7 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)
no code implementations • 19 Sep 2020 • Riccardo Marin, Simone Melzi, Emanuele Rodolà, Umberto Castellani
This augmentation provides an effective workaround for the resolution limitations imposed by the adopted morphable model.
1 code implementation • 14 Mar 2020 • Riccardo Marin, Arianna Rampini, Umberto Castellani, Emanuele Rodolà, Maks Ovsjanikov, Simone Melzi
We introduce the first learning-based method for recovering shapes from Laplacian spectra.
1 code implementation • 27 Jul 2018 • Riccardo Marin, Simone Melzi, Emanuele Rodolà, Umberto Castellani
We introduce a new method for non-rigid registration of 3D human shapes.