Jean-Sebastien Franco — accepted AI-conference papers

2020

Learning to Measure the Static Friction Coefficient in Cloth Contact

Abdullah Haroon Rasheed, Victor Romero, Florence Bertails-Descoubes, Stefanie Wuhrer, Jean-Sebastien Franco, Arnaud Lazarus

CVPR 2020oral

Measuring friction coefficients between cloth and an external body is a longstanding issue in mechanical engineering, never yet addressed with a pure vision-based system. The latter offers the prospect of simpler, less invasive friction measurement protocols compared to traditional ones, and can vas…

Cited by 21 PDF ↗Scholar ↗

2019

Moulding Humans: Non-Parametric 3D Human Shape Estimation From Single Images

Valentin Gabeur, Jean-Sebastien Franco, Xavier Martin, Cordelia Schmid, Gregory Rogez

ICCV 2019poster

In this paper, we tackle the problem of 3D human shape estimation from single RGB images. While the recent progress in convolutional neural networks has allowed impressive results for 3D human pose estimation, estimating the full 3D shape of a person is still an open issue. Model-based approaches ca…

Cited by 152 PDF ↗Scholar ↗

2018

Analyzing Clothing Layer Deformation Statistics of 3D Human Motions

Jinlong Yang, Jean-Sebastien Franco, Franck Hetroy-Wheeler, Stefanie Wuhrer

ECCV 2018poster

Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry, motion component and the clothing as a geometric layer. So far this clothing layer has only been used as raw offsets for i…

Cited by 76 Source ↗PDF ↗Scholar ↗

2018

Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency

Vincent Leroy, Jean-Sebastien Franco, Edmond Boyer

ECCV 2018poster

The rise of virtual and augmented reality fuels an increased need for content suitable to these new technologies including 3D contents obtained from real scenes. We consider in this paper the problem of 3D shape reconstruction from multi-view RGB images. We investigate the ability of learning-based…

Cited by 63 Source ↗PDF ↗Scholar ↗

2017

Multi-View Dynamic Shape Refinement Using Local Temporal Integration

Vincent Leroy, Jean-Sebastien Franco, Edmond Boyer

ICCV 2017poster

We consider 4D shape reconstructions in multi-view environments and investigate how to exploit temporal redundancy for precision refinement. In addition to being beneficial to many dynamic multi-view scenarios this also enables larger scenes where such increased precision can compensate for the redu…

Cited by 71 PDF ↗Scholar ↗

2017

Surface Motion Capture Transfer With Gaussian Process Regression

Adnane Boukhayma, Jean-Sebastien Franco, Edmond Boyer

CVPR 2017poster

We address the problem of transferring motion between captured 4D models. We particularly focus on human subjects for which the ability to automatically augment 4D datasets, by propagating movements between subjects, is of interest in a great deal of recent vision applications that builds on human v…

Cited by 14 PDF ↗Scholar ↗

2016

Volumetric 3D Tracking by Detection

Chun-Hao Huang, Benjamin Allain, Jean-Sebastien Franco, Nassir Navab, Slobodan Ilic, Edmond Boyer

CVPR 2016spotlight

In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations. On one hand, 3D tracking by detection has shown robust use in the context of interaction (Kinect) and surface tracking. On the other hand, volumetric representations have recently been p…

Cited by 40 PDF ↗Scholar ↗

2015

An Efficient Volumetric Framework for Shape Tracking

Benjamin Allain, Jean-Sebastien Franco, Edmond Boyer

CVPR 2015poster

Recovering 3D shape motion using visual information is an important problem with many applications in computer vision and computer graphics, among other domains. Most existing approaches rely on surface-based strategies, where surface models are fit to visual surface observations. While numerically…

Cited by 67 Source ↗PDF ↗Scholar ↗