232 citations · 284 across the 6 of their papers we have counts for
7 papers
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
We address the 3D reconstruction of human faces from a single RGB image. To this end, we propose Pixel3DMM, a set of highly-generalized vision transformers which predict per-pixel…
NPGA: Neural Parametric Gaussian Avatars
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
The creation of high-fidelity, digital versions of human heads is an important stepping stone in the process of further integrating virtual components into our everyday lives. Cons…
MonoNPHM: Dynamic Head Reconstruction from Monocular Videos
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos +3
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. To this end, we propose a latent appearance space that…
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
Jingwen Wang, Martin Rünz, Lourdes Agapito
We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the…
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
Martin Rünz, Maud Buffier, Lourdes Agapito
We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene.…
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
Martin Rünz, Lourdes Agapito
In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or se…