19 citations · 29 across the 6 of their papers we have counts for
7 papers
Virtual Elastic Objects
Hsiao-yu Chen, Edgar Tretschk, Tuur Stuyck +4
We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions.…
Advances in Neural Rendering
Ayush Tewari, Justus Thies, Ben Mildenhall +14
Synthesizing photo-realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are ge…
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik +3
We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a d…
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik +3
Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with a…
DispVoxNets: Non-Rigid Point Set Alignment with Supervised Learning Proxies
Soshi Shimada, Vladislav Golyanik, Edgar Tretschk +2
We introduce a supervised-learning framework for non-rigid point set alignment of a new kind - Displacements on Voxels Networks (DispVoxNets) - which abstracts away from the point…
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
Edgar Tretschk, Ayush Tewari, Michael Zollhöfer +2
Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder (DEMEA) which adds a novel embedded…