B01 | Adaptive Self-Consistent Visualization
Our long-term goal targets the adaptation of the computer-based visualization process in order to make the visualization outcome consistent with the input data and the objectives of the visualization itself. Therefore, this project contributes to addressing the key challenge of obtaining reliable visualizations. Our model of self-consistency is formulated in a quantitative fashion: how much does the visualization result deviate from the intended result? The adaptation optimizes parameters of the visualization pipeline in order to minimize the deviation, including perceptual models, techniques from computer vision, efficient optimization methods and support for multi-objective optimization.
Research Questions
How can we build an efficient optimization framework for the adaptation of self-consistent visualization techniques?
What kind of quantitative models from perceptual psychology and computer vision literature can be used to optimize the encoding of direction information in 2D visualization?
Which quantitative models are useful for optimizing animated visualizations with moving patterns?
How can we adapt the visualization to maintain the structure of relative differences or derivatives from the input data in the form of contrast of the visualization images?
How can multi-objective optimization be used for overlaid visualization of multivariate data?
Fig. 1: Readability evaluation of node-link diagrams of trajectories using different link types, which encode the direction. Here, a repeating comet texture is used to indicate the direction. The comet texture is adapted to the human perception of direction. Furthermore, node splatting is used to indicate node locations and spatial node density.
Publications
- J. Satkunarajan, M. Abdelaal, S. Koch, K. Kurzhals, and D. Weiskopf, “Can LLMs Simulate Target Users in Visualization Case Studies?,” Computer Graphics Forum, vol. 45, Art. no. 3, May 2026, doi: 10.1111/cgf.70446.
- M. Koch, N. Pathmanathan, D. Weiskopf, and K. Kurzhals, “A Multimodal Framework for Understanding Collaborative Design Processes,” IEEE Transactions on Visualization and Computer Graphics, vol. 32, Art. no. 1, 2026, doi: 10.1109/tvcg.2025.3634232.
- M. Koch et al., “Group Gaze-Sharing with Projection Displays,” in Proceedings of the 2025 Symposium on Eye Tracking Research and Applications, New York, NY, USA: ACM, 2025, pp. 92:1–92:7. doi: 10.1145/3715669.3725871.
- L. Zhou, X. Gou, and D. Weiskopf, “Continuous Indexed Points for Multivariate Volume Visualization,” Computational Visual Media, vol. 11, Art. no. 6, Dec. 2025, doi: 10.26599/cvm.2025.9450496.
- R. Jianu, N. Silva, N. Rodrigues, T. Blascheck, T. Schreck, and D. Weiskopf, “Gaze‐Aware Visualisation: Design Considerations and Research Agenda,” Computer Graphics Forum, 2025, doi: 10.1111/cgf.70097.
- M. Koch, N. Pathmanathan, D. Weiskopf, and K. Kurzhals, “How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications (ETRA ’24), New York, NY, USA: ACM, 2024, pp. 1–7. doi: 10.1145/3649902.3653349.
- D. Weiskopf, “Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in Light of Advanced AI,” in 2024 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), IEEE, Ed., IEEE, 2024, pp. 119–128. doi: 10.1109/beliv64461.2024.00019.
- F. Huth, M. Koch, M. Awad-Mohammed, K. Kurzhals, and D. Weiskopf, “Eye Tracking on Text Reading with Visual Enhancements,” in Symposium on Eye Tracking Research and Applications, in ETRA ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 7. doi: 10.1145/3649902.3653521.
- T. Rau et al., “Understanding Collaborative Learning of Molecular Structures in AR with Eye Tracking,” IEEE Computer Graphics and Applications, pp. 1–12, 2024, doi: 10.1109/mcg.2024.3503903.
- D. Klötzl, T. Krake, M. Becher, M. Koch, D. Weiskopf, and K. Kurzhals, “NMF-Based Analysis of Mobile Eye-Tracking Data,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, 2024, pp. 1–9. doi: 10.1145/3649902.3653518.
- N. Rodrigues, C. Schulz, S. Döring, D. Baumgartner, T. Krake, and D. Weiskopf, “Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, Jan. 2023, doi: 10.1109/TVCG.2022.3209429.
- K.-T. Chen et al., “Gazealytics : A Unified and Flexible Visual Toolkit for Exploratory and Comparative Gaze Analysis,” in ETRA ’23 : Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, New York, NY, USA: Association for Computing Machinery, May 2023, pp. 69:1–69:7. doi: 10.1145/3588015.3589844.
- F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, pp. 119–120, 2022, doi: 10.1515/itit-2022-0048.
- N. Rodrigues, C. Schulz, A. Lhuillier, and D. Weiskopf, “Cluster-Flow Parallel Coordinates: Tracing Clusters Across Subspaces,” in Proceedings of the Graphics Interface Conference (GI) (forthcoming), Canadian Human-Computer Communications Society / Société canadienne du dialogue humain-machine, 2020, pp. 0:1–0:11. doi: 10.20380/GI2020.38.
- M. Behrisch et al., “Quality Metrics for Information Visualization,” Computer Graphics Forum, vol. 37, Art. no. 3, 2018, doi: 10.1111/cgf.13446.
- K. Kurzhals, M. Stoll, A. Bruhn, and D. Weiskopf, “FlowBrush: Optical Flow Art,” in Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering (EXPRESSIVE, co-located with SIGGRAPH)., 2017, pp. 1:1–1:9. doi: 10.1145/3092912.3092914.
- K. Kurzhals, E. Çetinkaya, Y. Hu, W. Wang, and D. Weiskopf, “Close to the Action: Eye-Tracking Evaluation of Speaker-Following Subtitles,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, Ed., 2017, pp. 6559–6568. doi: 10.1145/3025453.3025772.
- R. Netzel, M. Burch, and D. Weiskopf, “Interactive Scanpath-Oriented Annotation of Fixations,” Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, pp. 183–187, 2016, doi: 10.1145/2857491.2857498.
- R. Netzel, M. Burch, and D. Weiskopf, “User Performance and Reading Strategies for Metro Maps: An Eye Tracking Study,” Special Issue on Eye Tracking for Spatial Research in Spatial Cognition and Computation: An Interdisciplinary Journal, 2016, doi: 10.1080/13875868.2016.1226839.
- K. Kurzhals, M. Hlawatsch, F. Heimerl, M. Burch, T. Ertl, and D. Weiskopf, “Gaze Stripes: Image-Based Visualization of Eye Tracking Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, Art. no. 1, 2016, doi: 10.1109/TVCG.2015.2468091.
- M. Burch, R. Woods, R. Netzel, and D. Weiskopf, “The Challenges of Designing Metro Maps,” Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016, doi: 10.5220/0005679601950202.
Project Group A
Models and Measures
Completed
Project Group B
Adaptive Algorithms
Completed
Project Group C
Interaction
Completed
Project Group D
Applications
Completed
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