54 citations · 164 across the 23 of their papers we have counts for
13 papers · 1 filter
Self-supervised adversarial masking for 3D point cloud representation learning
Michał Szachniewicz, Wojciech Kozłowski, Michał Stypułkowski +1
Self-supervised methods have been proven effective for learning deep representations of 3D point cloud data. Although recent methods in this domain often rely on random masking of…
MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation
Dominik Zimny, Artur Kasymov, Adam Kania +4
NeRF is a popular model that efficiently represents 3D objects from 2D images. However, vanilla NeRF has some important limitations. NeRF must be trained on each object separately.…
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
Wojciech Zając, Joanna Waczyńska, Piotr Borycki +3
Traditional 3D face models are based on mesh representations with texture. One of the most important models is FLAME (Faces Learned with an Articulated Model and Expressions), whic…
HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN
Adam Kania, Artur Kasymov, Jakub Kościukiewicz +4
The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated…
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
Michał Stypułkowski, Konstantinos Vougioukas, Sen He +3
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments i…
Two-headed eye-segmentation approach for biometric identification
Wiktor Lazarski, Maciej Zieba, Tanguy Jeanneau +2
Iris-based identification systems are among the most popular approaches for person identification. Such systems require good-quality segmentation modules that ideally identify the…
Flow Plugin Network for conditional generation
Patryk Wielopolski, Michał Koperski, Maciej Zięba
Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud gener…
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas
Przemysław Spurek, Sebastian Winczowski, Maciej Zięba +3
Recently proposed 3D object reconstruction methods represent a mesh with an atlas - a set of planar patches approximating the surface. However, their application in a real-world sc…
Representing Point Clouds with Generative Conditional Invertible Flow Networks
Michał Stypułkowski, Kacper Kania, Maciej Zamorski +3
In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation match…
HyperFlow: Representing 3D Objects as Surfaces
Przemysław Spurek, Maciej Zięba, Jacek Tabor +1
In this work, we present HyperFlow - a novel generative model that leverages hypernetworks to create continuous 3D object representations in a form of lightweight surfaces (meshes)…
UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree
Kacper Kania, Maciej Zięba, Tomasz Kajdanowicz
Signed distance field (SDF) is a prominent implicit representation of 3D meshes. Methods that are based on such representation achieved state-of-the-art 3D shape reconstruction qua…
Hypernetwork approach to generating point clouds
Przemysław Spurek, Sebastian Winczowski, Jacek Tabor +3
In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks. Contrary to the existing methods that learn only the representati…
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly +1
In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of im…