76 citations · 257 across the 98 of their papers we have counts for
11 papers · 2 filters
VeGaS: Video Gaussian Splatting
Weronika Smolak-Dyżewska, Dawid Malarz, Kornel Howil +3
Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to tra…
Neural Surface Priors for Editable Gaussian Splatting
Jakub Szymkowiak, Weronika Jakubowska, Dawid Malarz +5
In computer graphics and vision, recovering easily modifiable scene appearance from image data is crucial for applications such as content creation. We introduce a novel method tha…
PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz +3
Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstructi…
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
Artur Kasymov, Marcin Sendera, Michał Stypułkowski +2
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the…
MiraGe: Editable 2D Images using Gaussian Splatting
Joanna Waczyńska, Tomasz Szczepanik, Piotr Borycki +3
Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neur…
GASP: Gaussian Splatting for Physic-Based Simulations
Piotr Borycki, Weronika Smolak, Joanna Waczyńska +3
Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications. However, integrating with state-of-the-art 3D scene rendering techniques su…
GeoGuide: Geometric guidance of diffusion models
Mateusz Poleski, Jacek Tabor, Przemysław Spurek
Diffusion models are among the most effective methods for image generation. This is in particular because, unlike GANs, they can be easily conditioned during training to produce el…
D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup
Joanna Waczyńska, Piotr Borycki, Joanna Kaleta +2
Over the past years, we have observed an abundance of approaches for modeling dynamic 3D scenes using Gaussian Splatting (GS). Such solutions use GS to represent the scene's struct…
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski +3
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…
Deepfake for the Good: Generating Avatars through Face-Swapping with Implicit Deepfake Generation
Georgii Stanishevskii, Jakub Steczkiewicz, Tomasz Szczepanik +3
Numerous emerging deep-learning techniques have had a substantial impact on computer graphics. Among the most promising breakthroughs are the rise of Neural Radiance Fields (NeRFs)…
GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting
Joanna Waczyńska, Piotr Borycki, Sławomir Tadeja +2
Gaussian Splatting (GS) is a novel, state-of-the-art technique for rendering points in a 3D scene by approximating their contribution to image pixels through Gaussian distributions…