76 citations · 257 across the 98 of their papers we have counts for
5 papers · 2 filters
Gaussian Splatting with NeRF-based Color and Opacity
Dawid Malarz, Weronika Smolak, Jacek Tabor +2
Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential of neural networks to capture the intricacies of 3D objects. By encoding the shape and color information w…
MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation
Dominik Zimny, Artur Kasymov, Adam Kania +4
NeRF is a popular model that efficiently represents 3D objects from 2D images. However, vanilla NeRF has some important limitations. NeRF must be trained on each object separately.…
Gaussian model for closed curves
Krzysztof Byrski, Przemysław Spurek, Jacek Tabor
Gaussian Mixture Models (GMM) do not adapt well to curved and strongly nonlinear data. However, we can use Gaussians in the curvilinear coordinate systems to solve this problem. Mo…
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
Wojciech Zając, Joanna Waczyńska, Piotr Borycki +3
Traditional 3D face models are based on mesh representations with texture. One of the most important models is FLAME (Faces Learned with an Articulated Model and Expressions), whic…
HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN
Adam Kania, Artur Kasymov, Jakub Kościukiewicz +4
The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated…