Search Results for author: Riccardo Marin

Found 16 papers, 10 papers with code

CloSe: A 3D Clothing Segmentation Dataset and Model

no code implementations22 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.

Continual Learning Segmentation

Geometric Awareness in Neural Fields for 3D Human Registration

no code implementations21 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.

NSF: Neural Surface Fields for Human Modeling from Monocular Depth

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.

Computational Efficiency Virtual Try-on

Object pop-up: Can we infer 3D objects and their poses from human interactions alone?

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.

Object

Accelerating Transformer Inference for Translation via Parallel Decoding

3 code implementations17 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.

Machine Translation Translation

Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching

1 code implementation26 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.

Metric Based Few-Shot Graph Classification

1 code implementation8 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.

Data Augmentation Few-Shot Learning +3

Spectral Maps for Learning on Subgraphs

no code implementations30 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.

Graph Learning Knowledge Distillation

Interaction Replica: Tracking Human-Object Interaction and Scene Changes From Human Motion

no code implementations5 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.

Human-Object Interaction Detection Object +2

Smoothness and effective regularizations in learned embeddings for shape matching

1 code implementation14 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.

Relation

Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach

1 code implementation4 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.

valid

Shape registration in the time of transformers

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.

Correspondence Learning via Linearly-invariant Embedding

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)

3D Dense Shape Correspondence

High-Resolution Augmentation for Automatic Template-Based Matching of Human Models

no code implementations19 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.

Vocal Bursts Intensity Prediction

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