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Edgar Tretschk

4 accepted papers

2021

Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, Christian Theobalt
ICCV 2021poster

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 dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appea…

Cited by 557PDF ↗Scholar ↗
2020

DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects

Edgar Tretschk, Ayush Tewari, Michael Zollhöfer, Vladislav Golyanik, Christian Theobalt
ECCV 2020poster

Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling. We propose a general-purpose DEep MEsh Autoencoder \hbox{(DEMEA)} which adds a novel embedded deformation layer to a graph-convolutional mesh autoencoder. The embedded deformation layer (EDL) is a different…

Cited by 51Source ↗PDF ↗Scholar ↗
2020

Neural Dense Non-Rigid Structure from Motion with Latent Space Constraints

Vikramjit Sidhu, Edgar Tretschk, Vladislav Golyanik, Antonio Agudo, Christian Theobalt
ECCV 2020poster

We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach, which can be trained end-to-end in an unsupervised manner from 2D point tracks. Compared to the competing methods, our combination of loss functions is fully-differentiable and can be readily integrated into deep…

Cited by 66Source ↗PDF ↗Scholar ↗
2020

PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations

Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Carsten Stoll, Christian Theobalt
ECCV 2020poster

Implicit surface representation combined with deep learning has led to impressive models which can represent detailed shapes of objects. Implicit surface representations, such as signed-distance functions, allow to represent shapes of arbitrary topologies. Since a continous function is learned, the…

Cited by 116Source ↗PDF ↗Scholar ↗