39 citations · 53 across the 7 of their papers we have counts for
8 papers
EDGS: Eliminating Densification for Efficient Convergence of 3DGS
Dmytro Kotovenko, Olga Grebenkova, Björn Ommer
3D Gaussian Splatting reconstructs scenes by starting from a sparse Structure-from-Motion initialization and refining under-reconstructed regions. This process is slow, as it requi…
Does VLM Classification Benefit from LLM Description Semantics?
Pingchuan Ma, Lennart Rietdorf, Dmytro Kotovenko +2
Accurately describing images with text is a foundation of explainable AI. Vision-Language Models (VLMs) like CLIP have recently addressed this by aligning images and texts in a sha…
WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians
Dmytro Kotovenko, Olga Grebenkova, Nikolaos Sarafianos +8
While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches de…
CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians
Avinash Paliwal, Wei Ye, Jinhui Xiong +4
The field of 3D reconstruction from images has rapidly evolved in the past few years, first with the introduction of Neural Radiance Field (NeRF) and more recently with 3D Gaussian…
DepthFM: Fast Monocular Depth Estimation with Flow Matching
Ming Gui, Johannes Schusterbauer, Ulrich Prestel +6
Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transp…
Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes
Dmytro Kotovenko, Matthias Wright, Arthur Heimbrecht +1
There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However,…
A Content Transformation Block For Image Style Transfer
Dmytro Kotovenko, Artsiom Sanakoyeu, Pingchuan Ma +2
Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improve…
A Style-Aware Content Loss for Real-time HD Style Transfer
Artsiom Sanakoyeu, Dmytro Kotovenko, Sabine Lang +1
Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art h…