1 code implementation • 24 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.
no code implementations • 29 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.
no code implementations • 4 Jun 2019 • Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes
This task requires to fit an in-shop cloth image on the image of a person.
no code implementations • 8 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.
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.
no code implementations • 1 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.
no code implementations • 19 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.
1 code implementation • 30 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.
no code implementations • 21 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.
1 code implementation • NeurIPS 2023 • Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, Mickaël Chen, Alain Rakotomamonjy
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
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.