activity
20122024
most citedMeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy

1 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

GeoGuide: Geometric guidance of diffusion models

Mateusz Poleski, Jacek Tabor, Przemysław Spurek

Diffusion models are among the most effective methods for image generation. This is in particular because, unlike GANs, they can be easily conditioned during training to produce el…

cs.CV2024

HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation

Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski +3

Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…

eess.IV20231 cited

MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy

Łukasz Struski, Tomasz Urbańczyk, Krzysztof Bucki +4

Video generation is important, especially in medicine, as much data is given in this form. However, video generation of high-resolution data is a very demanding task for generative…

cs.CV20231 cited

Face Identity-Aware Disentanglement in StyleGAN

Adrian Suwała, Bartosz Wójcik, Magdalena Proszewska +3

Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age. Even though the state-of-the-art models successful…

cs.CV2023

Gaussian model for closed curves

Krzysztof Byrski, Przemysław Spurek, Jacek Tabor

Gaussian Mixture Models (GMM) do not adapt well to curved and strongly nonlinear data. However, we can use Gaussians in the curvilinear coordinate systems to solve this problem. Mo…

stat.ML20221 cited

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood

Piotr Tempczyk, Rafał Michaluk, Łukasz Garncarek +3

Most of the existing methods for estimating the local intrinsic dimension of a data distribution do not scale well to high-dimensional data. Many of them rely on a non-parametric n…