736 citations · 829 across the 6 of their papers we have counts for
8 papers
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…
Quantum Denoising Diffusion Models
Michael Kölle, Gerhard Stenzel, Jonas Stein +3
In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from conc…
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis
Azade Farshad, Yousef Yeganeh, Yu Chi +3
Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. While diffusion models con…
Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models
Robin Rombach, Andreas Blattmann, Björn Ommer
Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Of particular note is the field of ``AI-Art'', which has…
ArtFID: Quantitative Evaluation of Neural Style Transfer
Matthias Wright, Björn Ommer
The field of neural style transfer has experienced a surge of research exploring different avenues ranging from optimization-based approaches and feed-forward models to meta-learni…