14 citations · 22 across the 8 of their papers we have counts for
7 papers · 1 filter
Gaussian Splatting in Style
Abhishek Saroha, Mariia Gladkova, Cecilia Curreli +3
3D scene stylization extends the work of neural style transfer to 3D. A vital challenge in this problem is to maintain the uniformity of the stylized appearance across multiple vie…
Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes
George Eskandar, Youssef Farag, Tarun Yenamandra +3
A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success,…
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering
Mohammed Brahimi, Bjoern Haefner, Tarun Yenamandra +2
We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution mann…
Implicit Shape Completion via Adversarial Shape Priors
Abhishek Saroha, Marvin Eisenberger, Tarun Yenamandra +1
We present a novel neural implicit shape method for partial point cloud completion. To that end, we combine a conditional Deep-SDF architecture with learned, adversarial shape prio…
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions
Tarun Yenamandra, Ayush Tewari, Nan Yang +3
Neural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approache…
Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections
Zhenzhang Ye, Tarun Yenamandra, Florian Bernard +1
Graph matching aims to establish correspondences between vertices of graphs such that both the node and edge attributes agree. Various learning-based methods were recently proposed…
i3DMM: Deep Implicit 3D Morphable Model of Human Heads
Tarun Yenamandra, Ayush Tewari, Florian Bernard +4
We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expr…