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20182026
most citedExplicitly Minimizing the Blur Error of Variational Autoencoders

8 citations · 20 across the 15 of their papers we have counts for

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cs.CV2026

Diffusion Path Alignment for Long-Range Motion Generation and Domain Transitions

Haichao Wang, Alexander Okupnik, Yuxing Han +3

Long-range human movement generation remains a central challenge in computer vision and graphics. Generating coherent transitions across semantically distinct motion domains remain…

cs.CV2025

Generative human motion mimicking through feature extraction in denoising diffusion settings

Alexander Okupnik, Johannes Schneider, Kyriakos Flouris

Recent success with large language models has sparked a new wave of verbal human-AI interaction. While such models support users in a variety of creative tasks, they lack the embod…

cs.CV2025

Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI

Kyriakos Flouris, Moritz Halter, Yolanne Y. R. Lee +6

Hemodynamic analysis is essential for predicting aneurysm rupture and guiding treatment. While magnetic resonance flow imaging enables time-resolved volumetric blood velocity measu…

cs.CV2025

A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization

Mohammadreza Amirian, Michael Bach, Oscar Jimenez-del-Toro +18

Artificial intelligence (AI) has introduced numerous opportunities for human assistance and task automation in medicine. However, it suffers from poor generalization in the presenc…

cs.CV2023★ 8 cited

Explicitly Minimizing the Blur Error of Variational Autoencoders

Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya +2

Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have be…