2021

A proposed interactive workflow for our mixed-initiative approach. We arbitrarily chose the steps shown in this illustration.
Mijael Bueno, Elmar Eisemann, and Rafael Bidarra
In Proceedings of ICIDS, 2021
: Workflow of our manifold embedding: each image in a texture collection is run through the VGG16 network to obtain a feature vector extracted from the penultimate layer of the network. These features are then embedded in 2D space using the t-SNE algorithm.
Xuejiao Luo, Leonardo Scandolo, and Elmar Eisemann
Computer Graphics Forum, 2021
Left: Visible layer transitions where bin boundaries cut through primitives. Right: Smooth layer transitions improve the results but artifacts are still visible.
Fabian Friederichs, Martin Eisemann, and Elmar Eisemann
In Proceedings of Graphics Interface, 2021
Individually unique and stable immune fingerprints revealed by mass cytometry
Sanne E. de Jong, Vincent van Unen, Mikhael D. Manurung , Koen Stam, Jelle J. Goeman, et al.
Nat Immunol, 2021

2020

Remi van der Laan, Leonardo Scandolo, and Elmar Eisemann
ACM Comput Graph Interact Tech, 2020
An example render
Mathijs Molenaar and Elmar Eisemann
In Proceedings of Eurographics (Short Papers), 2020
Peiteng Shi, Markus Billeter, and Elmar Eisemann
Computers & Graphics, 2020
Jerry Guo, Martin Eisemann, and Elmar Eisemann
Computer Graphics Forum, 2020
Tom Callewaert, Jerry Guo, Guusje Harteveld, Abbie Vandivere, Elmar Eisemann, et al.
2020
Ruben Wiersma, Elmar Eisemann, and Klaus Hildebrandt
ACM Transactions on Graphics, 2020
Victor Careil, Markus Billeter, and Elmar Eisemann
Computer Graphics Forum, 2020
Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev, Thomas Höllt, Baldur van Lew, et al.
IEEE Transactions on Visualization and Computer Graphics, 2020
Xuejiao Luo, Nestor Z. Salamon, and Elmar Eisemann
IEEE Transactions on Visualization and Computer Graphics, 2020

2019

Gerard Simons, Sebastian Herholz, Victor Petitjean, Tobias Rapp, Marco Ament, et al.
Computer Graphics Forum, 2019
Cehao Yu, Elmar Eisemann, and Sylvia Pont
Perception, 2019
Leonardo Scandolo, Pablo Bauszat, and Elmar Eisemann
Computer Graphics Forum, 2019
Two armadillos (274k tetrahedra) in a pool of water (633k particles) simulated at 60 FPS with a time step of 1/60s. Fluid-deformable interaction and (self-)collisions are handled. The user can interact with the scene through click-and-dragging the meshes.
Christopher Brandt, Leonardo Scandolo, Elmar Eisemann, and Klaus Hildebrandt
ACM Transactions on Graphics, 2019
Left: single frame from 240Hz short-exposure video and a simulated long exposure at 30Hz by averaging 8 frames. Middle: Using the 240Hz input, our method enables mixing a long exposure in the periphery with a short exposure for the details on the pendulum. Via user annotations in the video, different shutter functions can be defined (top right). Annotations and shutter functions can be keyframed over time. Based on the annotations, our method defines an interpolated shutter function for each pixel (bottom right).
Nestor Z. Salamon, Markus Billeter, and Elmar Eisemann
Computer Graphics Forum, 2019
Indirect illumination computed from 1M animated virtual point lights (VPLs) with shadow maps of 162 resolution generated at interactive rates (100 ms, out of 194 ms for the image in total) by our many-view rendering algorithm (a). We show shadow maps of a subset of 2048 VPLs, for which many pixels are shared and rendered only once for multiple views (b). We highlight two close
Timothy R. Kol, Pablo Bauszat, Sungkil Lee, and Elmar Eisemann
Computer Graphics Forum, 2019
The components of LightGuider: (a) a 3D modeling view to place and modify luminaires, augmented with (b) a provenance tree, depicting several sequential modeling steps and parallel modeling branches, integrating information on the quality of the individual solutions, and providing guidance by pre-simulating and suggesting possible next steps to improve the design. A film-strip-like visualization (c) of screenshots helps to depict the evolution up to the currently selected state. A quality view (d) informs about the fulfillment level of the illumination constraints that need to be met, using bullet charts. Changing the weights of these constraints (e), and therefore, the lighting designer’s focus, triggers an update of the provenance tree node visualizations (reflecting the weights of the constraints in the distribution of the treemap space). Moreover, the defined weights are also considered in the generation of new suggestions, which are tailored towards satisfying constraints with higher weights.
Andreas Walch, Michael Schwärzler, Christan Luksch, Elmar Eisemann, and Theresia Gschwandtner
CoRR, 2019