Publications de Benoît Alain René Guillard

A Latent Implicit 3D Shape Model for Multiple Levels of Detail

Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single level of detail. Emulating low levels of detail can be achieved with shallow ...

Springer Nature (Switzerland)2025

Garment Recovery with Shape and Deformation Priors

Pascal Fua, Benoît Alain René Guillard, Ren Li, Corentin Camille Marius Dumery

While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or defor ...

2024

DeepMesh: Differentiable Iso-Surface Extraction

Pascal Fua, Timur Bagautdinov, Pierre Bruno Baqué, Edoardo Remelli, Benoît Alain René Guillard, Pierre Mathieu Yvernay

2024

DrapeNet: Garment Generation and Self-Supervised Draping

Pascal Fua, Mathieu Salzmann, Benoît Alain René Guillard, Ren Li, Luca De Luigi

Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets. However, they are designed to train one network per clothing item, which severely limits their generalization a ...

IEEE2023

ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns

Pascal Fua, Benoît Alain René Guillard, Ren Li

Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are draped. However, they are either unable to handle multi-layered clothing, wh ...

2023

Deep Learning for 3D Surface Modelling and Reconstruction

Benoît Alain René Guillard

In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

EPFL2023

Latent Representation of Computational Fluid Dynamics Meshes and Application to Airfoil Aerodynamics

Pascal Fua, Benoît Alain René Guillard, Zhen Wei

Mesh manipulation is central to computational fluid dynamics. However, creating appropriate computational meshes often involves substantial manual intervention that has to be repeated each time the target shape changes. To address this problem, we propose ...

AMER INST AERONAUTICS ASTRONAUTICS2023

Latent Representation of CFD Meshes and Application to Airfoil Aerodynamics

Pascal Fua, Benoît Alain René Guillard, Zhen Wei

Mesh manipulation is central to Computational Fluid Dynamics (CFD). However, creating appropriate computational meshes often involves substantial manual intervention that has to be repeated each time the target shape changes. To address this problem, we p ...

American Institute of Aeronautics and Astronautics2023

MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks

Pascal Fua, Benoît Alain René Guillard, Federico Stella

Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces. However, current approaches to converting them into explicit meshes tend to either be expensive or to degrade the accuracy. Here, we extend the marching cube algorithm to han ...

Springer2022

DIG: Draping Implicit Garment over the Human Body

Pascal Fua, Edoardo Remelli, Benoît Alain René Guillard, Ren Li

Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable. To address these limitations, we propose an end-to-end differentiabl ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Learning to Simulate Realistic LiDARs

Pascal Fua, Benoît Alain René Guillard

Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation ...

2022

Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches

Pascal Fua, Edoardo Remelli, Benoît Alain René Guillard, Pierre Mathieu Yvernay

Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information. In this paper, we use an encoder/decoder architecture for the sketch to mesh translation. When integrated into a ...

IEEE2021

UCLID-Net: Single View Reconstruction in Object Space

Pascal Fua, Edoardo Remelli, Benoît Alain René Guillard

Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean st ...

2020

MeshSDF: Differentiable Iso-Surface Extraction

Pascal Fua, Timur Bagautdinov, Pierre Bruno Baqué, Edoardo Remelli, Benoît Alain René Guillard, Artem Lukoianov

2020