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Found 11 papers, 3 papers with code
MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
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.
Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach
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.
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
This task requires to fit an in-shop cloth image on the image of a person.
Learning disconnected manifolds: a no GANs land
Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator.
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On
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.
Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection
Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting disconnected manifolds.
Latent reweighting, an almost free improvement for GANs
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.
EdiBERT, a generative model for image editing
Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks.
Unveiling the Latent Space Geometry of Push-Forward Generative Models
We investigate the relationship between the performance of these models and the geometry of their latent space.
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
Learning disconnected manifolds: a no GAN's land
Typical architectures of Generative Adversarial Networks make use of a unimodal latent/input distribution transformed by a continuous generator.
