genwro.AI

We are a research group specializing in artificial intelligence, with a particular focus on machine learning and generative models

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About us

Through constant innovation and continuous pursuit of excellence, we strive to redefine the boundaries of what is possible in the world of artificial intelligence. Our pioneering research drives the development of cutting-edge algorithms and techniques, enabling the creation of more reliable and intelligent machine learning systems.

Collaborating with research centers

We cooperate with various research centers, including University of Cambridge, Imperial College London, Tooploox, Warsaw University of Technology, ETH Zurich, UCD Dublin, GMUM, Jagiellonian University in Kraków.

Publishing in top conferences

We consistently contribute research showcased at premier AI conferences like NeurIPS, AAAI, ICML, ICLR.

Working with various technologies

We are a team of ML researchers focused on: Generative models, 3D representation learning, Few-shot learning, Uncertainty estimation and out-of-distribution detection, omputer vision (segmentation, image restoration, image and video generation)

Published publications

Total citations

Projects

milion project co-financing

Team

Maciej Zięba

Maciej Zięba

DSc, PhD, genwro.AI group leader
Kamil Adamczewski

Kamil Adamczewski

PhD
Michał Stypułkowski

Michał Stypułkowski

PhD
Patryk Wielopolski

Patryk Wielopolski

PhD Candidate
Przemysław Dolata

Przemysław Dolata

PhD Candidate
Weronika Jakubowska

Weronika Jakubowska

PhD Candidate
Dawid Migacz

Dawid Migacz

PhD Candidate
Oleksii Furman

Oleksii Furman

PhD candidate
Katarzyna Jabłońska

Katarzyna Jabłońska

PhD Candidate
Wojciech Kozłowski

Wojciech Kozłowski

PhD Candidate
Mateusz Grzesiuk

Mateusz Grzesiuk

PhD Candidate
Marcin Kostrzewa

Marcin Kostrzewa

PhD Candidate
Radosław Kuczbański

Radosław Kuczbański

PhD Candidate
Łukasz Lenkiewicz

Łukasz Lenkiewicz

MSc student
Konrad Karanowski

Konrad Karanowski

Msc
Jakub Balicki

Jakub Balicki

Msc

Publications

Explore a selection of our research papers published at leading machine learning conferences and journals.

  • All
  • 2025
  • 2024
  • 2023
  • 2022
  • 2021
FlowHMM: Flow-based continuous hidden Markov models

FlowHMM

NeurIPS 2022

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models

Plugen

AAAI 2022

Non-Gaussian Gaussian Processes
                for Few-Shot Regression

NGGP

NeurIPS 2021

Diffused Heads: Diffusion Models Beat GANs
                on Talking-Face Generation

Diffused Heads

WACV 2024

MineCam

MineCam

Remote Sensing

MineCam

PPCEF

ECAI 2024

MineCam

NodeFlow

Entropy

HyperShot: Few-Shot Learning by Kernel HyperNetworks

HyperShot

WACV 2023

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression

TreeFlow

ECAI 2023

KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation

KeyFace

CVPR 2025

MultiPlaneNeRF: Neural Radiance Field with
                  Non-Trainable Representation

MultiPlaneNeRF

Expert Systems with Applications

Neural Surface Priors for Editable Gaussian Splatting

Neural Surface Priors

IJCNN 2025

SupResDiffGAN a new approach for the Super-Resolution task

SupResDiffGAN

ICCS 2025

Satellite Data Based One-Class Classifier Models with Augmentation for Efficient Mineral Exploration

Models for Mineral Exploration

ACIIDS 2025

Direct detection of elongated objects geometry via a centerline-based representation

Detection via a centerline representation

ACIIDS 2025

Image enhancement with Boosted Schrödinger Bridge

Boosted Schrödinger Bridge

ACIIDS 2025