57 citations · 201 across the 21 of their papers we have counts for
12 papers · 1 filter
Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Images
Alexander Vieth, Boudewijn Lelieveldt, Elmar Eisemann +2
High-dimensional images, or images with a high-dimensional attribute vector per pixel, are commonly explored with coordinated views of a low-dimensional embedding of the attribute…
Surface-Aware Distilled 3D Semantic Features
Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
Many 3D tasks such as pose alignment, animation, motion transfer, and 3D reconstruction rely on establishing correspondences between 3D shapes. This challenge has recently been app…
Uncertainty for SVBRDF Acquisition using Frequency Analysis
Ruben Wiersma, Julien Philip, Miloš Hašan +4
This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the o…
MotionDreamer: Exploring Semantic Video Diffusion features for Zero-Shot 3D Mesh Animation
Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
Animation techniques bring digital 3D worlds and characters to life. However, manual animation is tedious and automated techniques are often specialized to narrow shape classes. In…
RANRAC: Robust Neural Scene Representations via Random Ray Consensus
Benno Buschmann, Andreea Dogaru, Elmar Eisemann +2
Learning-based scene representations such as neural radiance fields or light field networks, that rely on fitting a scene model to image observations, commonly encounter challenges…
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformat…