2 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…