Hello!
I am Benoît, a researcher at 
Neural Concept. I graduated in 2023 from my PhD at EPFL’s CvLab, supervised by Prof. Pascal Fua.
My work is focused on finding good representations for 3D surface reconstruction and manipulation with neural networks.
I was a research intern at Microsoft Research in 2021 and Meta Reality Labs in 2022.
Publications
(full list on scholar)
DrapeNet: Garment Generation and Self-Supervised Draping
Ren Li*, Luca De Luigi*, Benoit Guillard, Mathieu Salzmann, Pascal Fua ; at CVPR 2023 ( * indicates equal contributions)
We use MeshUDF as a learned parameterization of garments, and learn to drape them without supervision - in the manner of SNUG, but with a single network for a whole category.
DIG: Draping Implicit Garment over the Human Body
Ren Li, Benoit Guillard, Edoardo Remelli, Pascal Fua ; at ACCV 2022 (Oral)
Reconstructing and draping garments over a parametric body model, with neural network trained to avoid collisions.
Learning to Simulate Realistic LiDARs
Benoit Guillard, Sai Vemprala, Jayesh K. Gupta, Ondrej Miksik, Vibhav Vineet, Pascal Fua, Ashish Kapoor ; at IROS 2022
A neural network to simulate how objects interact with LiDARs given their appearance, deployed to a standard driving simulator.
MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks
Benoit Guillard, Federico Stella, Pascal Fua ; at ECCV 2022
An extension of Marching Cubes to mesh non-watertight surfaces, applied the output of Unsigned Distance Field networks. We also derive gradients for the reconstructed vertex positions wrt. the UDF field.
Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches
Benoit Guillard*, Edoardo Remelli*, Pierre Yvernay, Pascal Fua ; at ICCV 2021 ( * indicates equal contributions)
A deep learning based pipeline to reconstruct and locally edit 3D shapes from sketches.
UCLID-Net: Single View Reconstruction in Object Space
Benoit Guillard, Edoardo Remelli, Pascal Fua ; at NeurIPS 2020
A network architecture mixing 3D convolutions and local surface patches for reconstructing object from a single image.
Past, unpublished work
AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors
Matthew Z Wong*, Benoit Guillard*, Riku Murai, Sajad Saeedi, Paul Kelly ( * indicates equal contributions)
A low-level and super lightweight CNN implementation running directly on the analog light sensor of a special camera, SCAMP5.
Academic services
I was a reviewer for CVPR (2022 best reviewer award), ICCV, ECCV, ACCV, NeurIPS (2022 top reviewer), ICLR, ICML, SIGGRAPH, SIGGRAPH Asia, C&G, WACV, Eurographics.