Search Results for author: Thibaut Issenhuth

Found 10 papers, 2 papers with code

Learning disconnected manifolds: a no GAN's land

no code implementations ICML 2020 Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary

Typical architectures of Generative Adversarial Networks make use of a unimodal latent/input distribution transformed by a continuous generator.

Optimal precision for GANs

no code implementations21 Jul 2022 Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary, David Picard

We investigate the relationship between the performance of these models and the geometry of their latent space.

Face Model

EdiBERT, a generative model for image editing

1 code implementation30 Nov 2021 Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary, David Picard

Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks.

Image Denoising Image Manipulation

Latent reweighting, an almost free improvement for GANs

no code implementations19 Oct 2021 Thibaut Issenhuth, Ugo Tanielian, David Picard, Jeremie Mary

Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting different classes of images.

Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection

no code implementations1 Jan 2021 Thibaut Issenhuth, Ugo Tanielian, David Picard, Jeremie Mary

Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting disconnected manifolds.

Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On

no code implementations ECCV 2020 Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes

This task requires fitting an in-shop cloth image on the image of a person, which is highly challenging because it involves cloth warping, image compositing, and synthesizing.

Image Generation Virtual Try-on

Learning disconnected manifolds: a no GANs land

no code implementations8 Jun 2020 Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary

Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator.

Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach

no code implementations29 Nov 2018 Thibaut Issenhuth, Vinkle Srivastav, Afshin Gangi, Nicolas Padoy

Methods: We propose a comparison of 6 state-of-the-art face detectors on clinical data using Multi-View Operating Room Faces (MVOR-Faces), a dataset of operating room images capturing real surgical activities.

Domain Adaptation Face Detection

MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation

1 code implementation24 Aug 2018 Vinkle Srivastav, Thibaut Issenhuth, Abdolrahim Kadkhodamohammadi, Michel de Mathelin, Afshin Gangi, Nicolas Padoy

In this paper, we present the dataset, its annotations, as well as baseline results from several recent person detection and 2D/3D pose estimation methods.

3D Human Pose Estimation 3D Pose Estimation +2

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