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"authors": [ {"first" : "Fairouz", "last" : "Grioui"}, {"first" : "Pantelis", "last" : "Antoniadis"}, {"first" : "Xingyao", "last" : "Yu"}, {"first" : "Tanja", "last" : "Blascheck"} ], "doi" : "10.2312/evp.20241090", "bibtexKey": "noauthororeditor2024personal" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/24879d85b7fa8ecabd625e0b00e178a48/msedlmair", "tags" : [ "myown","simtech","vis(us)","visus:sedlmaml","exc2075","exc2075(from2019)" ], "intraHash" : "4879d85b7fa8ecabd625e0b00e178a48", "interHash" : "dc36cdc101c0105f6b4c40b65d985d81", "label" : "cARdLearner: Using Expressive Virtual Agents when Learning Vocabulary in Augmented Reality", "user" : "msedlmair", "description" : "", "date" : "2023-11-21 17:34:06", "changeDate" : "2023-11-21 17:34:06", "count" : 11, "pub-type": "inproceedings", "booktitle": "ACM Conference on Human Factors in Computing Systems Extended Abstracts (CHI-EA))", "year": "2022", "url": "https://doi.org/10.1145/3491101.3519631", "author": [ "Aimee Sousa Calepso","Natalie Hube","Noah Berenguel Senn","Vincent Brandt","Michael Sedlmair" ], "authors": [ {"first" : "Aimee", "last" : "Sousa Calepso"}, {"first" : "Natalie", "last" : "Hube"}, {"first" : "Noah", "last" : "Berenguel Senn"}, {"first" : "Vincent", "last" : "Brandt"}, {"first" : "Michael", "last" : "Sedlmair"} ], "isbn" : "9781450391566", "numpages" : "6", "articleno" : "245", "location" : "New Orleans, LA, USA", "doi" : "10.1145/3491101.3519631", "bibtexKey": "calepso2022cardlearner" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c9efd9e4eddc4db6dc9aaf808b170f2a/msedlmair", "tags" : [ "myown","simtech","vis(us)","visus:sedlmaml","exc2075","exc2075(from2019)" ], "intraHash" : "c9efd9e4eddc4db6dc9aaf808b170f2a", "interHash" : "642477c3aabc971a9d5db8bf800f1fc8", "label" : "AR Hero: Generating Interactive Augmented Reality Guitar Tutorials", "user" : "msedlmair", "description" : "", "date" : "2023-11-21 17:26:56", "changeDate" : "2023-11-21 17:26:56", "count" : 11, "pub-type": "inproceedings", "booktitle": "2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)", "year": "2022", "url": "", "author": [ "Lucchas Ribeiro Skreinig","Ana Stanescu","Shohei Mori","Frank Heyen","Peter Mohr","Michael Sedlmair","Dieter Schmalstieg","Denis Kalkofen" ], "authors": [ {"first" : "Lucchas Ribeiro", "last" : "Skreinig"}, {"first" : "Ana", "last" : "Stanescu"}, {"first" : "Shohei", "last" : "Mori"}, {"first" : "Frank", "last" : "Heyen"}, {"first" : "Peter", "last" : "Mohr"}, {"first" : "Michael", "last" : "Sedlmair"}, {"first" : "Dieter", "last" : "Schmalstieg"}, {"first" : "Denis", "last" : "Kalkofen"} ], "pages": "395-401","abstract": "We introduce a system capable of generating interactive Aug-mented Reality guitar tutorials by parsing common digital guitar tablature and by capturing the performance of an expert using a multi-camera array. Instructions are presented to the user in an Augmented Reality application using either an abstract visualization, a 3D virtual hand, or a 3D video. To support individual users at different skill levels the system provides full control of the play-back of a tutorial, including its speed and looping behavior, while delivering live feedback on the user's performance.", "doi" : "10.1109/VRW55335.2022.00086", "bibtexKey": "9757565" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b8da417e8aaf9d7f9cc4030b1054fd94/msedlmair", "tags" : [ "myown","simtech","vis(us)","visus:sedlmaml","EXC2075","exc2075","exc2075(from2019)" ], "intraHash" : "b8da417e8aaf9d7f9cc4030b1054fd94", "interHash" : "c491444b6f8fdccd47fc036bd0601481", "label" : "Using Expressive Avatars to Increase Emotion Recognition: A Pilot Study", "user" : "msedlmair", "description" : "", "date" : "2023-11-21 17:10:46", "changeDate" : "2023-11-21 17:10:46", "count" : 6, "pub-type": "inproceedings", "booktitle": "Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems","series": "CHI EA '22","publisher":"Association for Computing Machinery","address":"New York, NY, USA", "year": "2022", "url": "https://doi.org/10.1145/3491101.3519822", "author": [ "Natalie Hube","Kresimir Vidackovic","Michael Sedlmair" ], "authors": [ {"first" : "Natalie", "last" : "Hube"}, {"first" : "Kresimir", "last" : "Vidackovic"}, {"first" : "Michael", "last" : "Sedlmair"} ], "pages": "1\u20137","abstract": "Virtual avatars are widely used for collaborating in virtual environments. Yet, often these avatars lack expressiveness to determine a state of mind. Prior work has demonstrated effective usage of determining emotions and animated lip movement through analyzing mere audio tracks of spoken words. To provide this information on a virtual avatar, we created a natural audio data set consisting of 17 audio files from which we then extracted the underlying emotion and lip movement. To conduct a pilot study, we developed a prototypical system that displays the extracted visual parameters and then maps them on a virtual avatar while playing the corresponding audio file. We tested the system with 5 participants in two conditions: (i) while seeing the virtual avatar only an audio file was played. (ii) In addition to the audio file, the extracted facial visual parameters were displayed on the virtual avatar. Our results suggest the validity of using additional visual parameters in the avatars\u2019 face as it helps to determine emotions. We conclude with a brief discussion on the outcomes and their implications on future work.", "isbn" : "9781450391566", "location" : "New Orleans, LA, USA", "doi" : "10.1145/3491101.3519822", "bibtexKey": "Hube2022" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/27e724b83a7eb0255a8b94e75da9bc0c3/xingyaoyu", "tags" : [ "pn7-1","myown","simtech","pn7-1.3","theochem:koehn","EXC2075","visus","exc2075","exc2075(from2019)","koehn","EXC2075(from2019)","vis(us)","visus:sedlmaml","pn7","visus:yuxo" ], "intraHash" : "7e724b83a7eb0255a8b94e75da9bc0c3", "interHash" : "1accfcc7c6674ce6c6b8c00162b5daf1", "label" : "MolecuSense: Using Force-Feedback Gloves for Creating and Interacting with Ball-and-Stick Molecules in VR", "user" : "xingyaoyu", "description" : "", "date" : "2022-11-09 16:13:12", "changeDate" : "2024-09-05 14:52:24", "count" : 18, "pub-type": "inproceedings", "booktitle": "Proceedings of the 15th International Symposium on Visual Information Communication and Interaction","series": "VINCI '22","publisher":"Association for Computing Machinery","address":"New York, NY, USA", "year": "2022", "url": "https://doi.org/10.1145/3554944.3554956", "author": [ "Patrick Gebhardt","Xingyao Yu","Andreas Köhn","Michael Sedlmair" ], "authors": [ {"first" : "Patrick", "last" : "Gebhardt"}, {"first" : "Xingyao", "last" : "Yu"}, {"first" : "Andreas", "last" : "Köhn"}, {"first" : "Michael", "last" : "Sedlmair"} ], "pages": "1\u20135","abstract": "We contribute MolecuSense, a virtual version of a physical molecule construction kit, based on visualization in Virtual Reality (VR) and interaction with force-feedback gloves. Targeting at chemistry education, our goal is to make virtual molecule structures more tangible. Results of an initial user study indicate that the VR molecular construction kit was positively received. Compared to a physical construction kit, the VR molecular construction kit is on the same level in terms of natural interaction. Besides, it fosters the typical digital advantages though, such as saving, exporting, and sharing of molecules. Feedback from the study participants has also revealed potential future avenues for tangible molecule visualizations.", "isbn" : "9781450398060", "location" : "Chur, Switzerland", "doi" : "10.1145/3554944.3554956", "bibtexKey": "Gebhardt2022" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/287f3a38e8d43c2711bff361e7c7e8a59/katharinafuchs", "tags" : [ "myown","simtech","peerReviewed","vis(us)","pn6","visus:ertl","visus:tkachegb","EXC2075","visus","from:gtkachev","visus:freysn" ], "intraHash" : "87f3a38e8d43c2711bff361e7c7e8a59", "interHash" : "347d569c3aae7cd4934c93a157da3cfe", "label" : "S4: Self-Supervised learning of Spatiotemporal Similarity", "user" : "katharinafuchs", "description" : "", "date" : "2021-12-08 17:10:08", "changeDate" : "2021-12-08 16:10:08", "count" : 11, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2021", "url": "", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "abstract": "We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.", "isbn" : "10.1109/TVCG.2021.3101418", "bibtexKey": "tkachev2021selfsupervised" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/28b2c9006e55a886ca79f64b1302c9e1c/katharinafuchs", "tags" : [ "simtech","peerReviewed","vis(us)","pn6","visus:ertl","visus:tkachegb","EXC2075","visus","from:gtkachev","visus:freysn" ], "intraHash" : "8b2c9006e55a886ca79f64b1302c9e1c", "interHash" : "c658c4630195f65d590c2bf52b163201", "label" : "Local Prediction Models for Spatiotemporal Volume Visualization", "user" : "katharinafuchs", "description" : "", "date" : "2021-12-08 17:10:08", "changeDate" : "2021-12-08 16:10:08", "count" : 10, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2019", "url": "http://ieeexplore.ieee.org/document/8941308", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "abstract": "We present a machine learning-based approach for detecting and visualizing complex behavior in spatiotemporal volumes. For this, we train models to predict future data values at a given position based on the past values in its neighborhood, capturing common temporal behavior in the data. We then evaluate the model's prediction on the same data. High prediction error means that the local behavior was too complex, unique or uncertain to be accurately captured during training, indicating spatiotemporal regions with interesting behavior. By training several models of varying capacity, we are able to detect spatiotemporal regions of various complexities. We aggregate the obtained prediction errors into a time series or spatial volumes and visualize them together to highlight regions of unpredictable behavior and how they differ between the models. We demonstrate two further volumetric applications: adaptive timestep selection and analysis of ensemble dissimilarity. We apply our technique to datasets from multiple application domains and demonstrate that we are able to produce meaningful results while making minimal assumptions about the underlying data.", "issn" : "2160-9306", "doi" : "10.1109/TVCG.2019.2961893", "bibtexKey": "tkachev2019local" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f7f10eed0e7eaef396f88a3c4fa1492b/katharinafuchs", "tags" : [ "from:xingyaoyu","myown","EXC2075(from2019)","simtech","vis(us)","visus:sedlmaml","visus:angerbkn","EXC2075","visus","exc2075(from2019)" ], "intraHash" : "f7f10eed0e7eaef396f88a3c4fa1492b", "interHash" : "9cd00c8718ffc145ad4bd0452f44c163", "label" : "Perspective Matters: Design Implications for Motion Guidance in Mixed Reality", "user" : "katharinafuchs", "description" : "", "date" : "2021-12-08 17:10:08", "changeDate" : "2021-12-08 16:10:08", "count" : 14, "pub-type": "inproceedings", "booktitle": "Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR)", "year": "2020", "url": "https://ieeexplore.ieee.org/abstract/document/9284729", "author": [ "Xingyao Yu","Katrin Angerbauer","Peter Mohr","Denis Kalkofen","Michael Sedlmair" ], "authors": [ {"first" : "Xingyao", "last" : "Yu"}, {"first" : "Katrin", "last" : "Angerbauer"}, {"first" : "Peter", "last" : "Mohr"}, {"first" : "Denis", "last" : "Kalkofen"}, {"first" : "Michael", "last" : "Sedlmair"} ], "abstract": "We investigate how Mixed Reality (MR) can be used to guide human body motions, such as in physiotherapy, dancing, or workout applications. While first MR prototypes have shown promising results, many dimensions of the design space behind such applications remain largely unexplored. To better understand this design space, we approach the topic from different angles by contributing three user studies. In particular, we take a closer look at the influence of the perspective, the characteristics of motions, and visual guidance on different user performance measures. Our results indicate that a first-person perspective performs best for all visible motions, whereas the type of visual instruction plays a minor role. From our results we compile a set of considerations that can guide future work on the design of instructions, evaluations, and the technical setup of MR motion guidance systems.", "doi" : "10.1109/ISMAR50242.2020.00085", "bibtexKey": "yu2020perspective" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/287f3a38e8d43c2711bff361e7c7e8a59/gtkachev", "tags" : [ "myown","simtech","visus:ertl","EXC2075","visus","peerReviewed","vis(us)","pn6","visus:tkachegb","visus:freysn" ], "intraHash" : "87f3a38e8d43c2711bff361e7c7e8a59", "interHash" : "347d569c3aae7cd4934c93a157da3cfe", "label" : "S4: Self-Supervised learning of Spatiotemporal Similarity", "user" : "gtkachev", "description" : "", "date" : "2021-10-04 19:04:24", "changeDate" : "2021-10-04 17:04:24", "count" : 11, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2021", "url": "", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "abstract": "We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.", "isbn" : "10.1109/TVCG.2021.3101418", "bibtexKey": "tkachev2021selfsupervised" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f7f10eed0e7eaef396f88a3c4fa1492b/xingyaoyu", "tags" : [ "pn7-1","myown","simtech","visus:angerbkn","pn7-1.3","EXC2075","visus","exc2075","exc2075(from2019)","EXC2075(from2019)","vis(us)","visus:sedlmaml","pn7","visus:yuxo" ], "intraHash" : "f7f10eed0e7eaef396f88a3c4fa1492b", "interHash" : "9cd00c8718ffc145ad4bd0452f44c163", "label" : "Perspective Matters: Design Implications for Motion Guidance in Mixed Reality", "user" : "xingyaoyu", "description" : "", "date" : "2020-12-27 00:27:45", "changeDate" : "2024-09-05 14:52:52", "count" : 14, "pub-type": "inproceedings", "booktitle": "Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR)", "year": "2020", "url": "https://ieeexplore.ieee.org/abstract/document/9284729", "author": [ "Xingyao Yu","Katrin Angerbauer","Peter Mohr","Denis Kalkofen","Michael Sedlmair" ], "authors": [ {"first" : "Xingyao", "last" : "Yu"}, {"first" : "Katrin", "last" : "Angerbauer"}, {"first" : "Peter", "last" : "Mohr"}, {"first" : "Denis", "last" : "Kalkofen"}, {"first" : "Michael", "last" : "Sedlmair"} ], "pages": "577-587","abstract": "We investigate how Mixed Reality (MR) can be used to guide human body motions, such as in physiotherapy, dancing, or workout applications. While first MR prototypes have shown promising results, many dimensions of the design space behind such applications remain largely unexplored. To better understand this design space, we approach the topic from different angles by contributing three user studies. In particular, we take a closer look at the influence of the perspective, the characteristics of motions, and visual guidance on different user performance measures. Our results indicate that a first-person perspective performs best for all visible motions, whereas the type of visual instruction plays a minor role. From our results we compile a set of considerations that can guide future work on the design of instructions, evaluations, and the technical setup of MR motion guidance systems.", "doi" : "10.1109/ISMAR50242.2020.00085", "bibtexKey": "yu2020perspective" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b94a323bb1481f5b322255b9bf245115/frankheyen", "tags" : [ "myown","sfbtrr161","simtech","visus:heyenfk","EXC2075","from:frankheyen","visus","visus:weiskopf","visus:munzta","peerReviewed","vis(us)","visus:sedlmaml" ], "intraHash" : "b94a323bb1481f5b322255b9bf245115", "interHash" : "5bcaeedc1ee8e009a637fa403fc78357", "label" : "ClaVis: An Interactive Visual Comparison System for Classifiers", "user" : "frankheyen", "description" : "", "date" : "2020-10-20 13:38:55", "changeDate" : "2022-08-08 13:53:08", "count" : 17, "pub-type": "inproceedings", "booktitle": "Proceedings of the International Conference on Advanced Visual Interfaces","series": "AVI '20","publisher":"Association for Computing Machinery","address":"New York, NY, USA", "year": "2020", "url": "https://doi.org/10.1145/3399715.3399814", "author": [ "Frank Heyen","Tanja Munz","Michael Neumann","Daniel Ortega","Ngoc Thang Vu","Daniel Weiskopf","Michael Sedlmair" ], "authors": [ {"first" : "Frank", "last" : "Heyen"}, {"first" : "Tanja", "last" : "Munz"}, {"first" : "Michael", "last" : "Neumann"}, {"first" : "Daniel", "last" : "Ortega"}, {"first" : "Ngoc Thang", "last" : "Vu"}, {"first" : "Daniel", "last" : "Weiskopf"}, {"first" : "Michael", "last" : "Sedlmair"} ], "abstract": "We propose ClaVis, a visual analytics system for comparative analysis of classification models. ClaVis allows users to visually compare the performance and behavior of tens to hundreds of classifiers trained with different hyperparameter configurations. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that reveals correlations between parameters and scores, and a training history chart. We demonstrate the effectivity of our approach in multiple case studies for training classification models in the domain of natural language processing.", "isbn" : "9781450375351", "numpages" : "9", "articleno" : "9", "location" : "Salerno, Italy", "doi" : "10.1145/3399715.3399814", "bibtexKey": "10.1145/3399715.3399814" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/28b2c9006e55a886ca79f64b1302c9e1c/gtkachev", "tags" : [ "simtech","visus:ertl","EXC2075","visus","peerReviewed","vis(us)","pn6","visus:tkachegb","visus:freysn" ], "intraHash" : "8b2c9006e55a886ca79f64b1302c9e1c", "interHash" : "c658c4630195f65d590c2bf52b163201", "label" : "Local Prediction Models for Spatiotemporal Volume Visualization", "user" : "gtkachev", "description" : "", "date" : "2020-01-10 13:37:33", "changeDate" : "2021-12-06 11:27:06", "count" : 10, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2019", "url": "http://ieeexplore.ieee.org/document/8941308", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "abstract": "We present a machine learning-based approach for detecting and visualizing complex behavior in spatiotemporal volumes. For this, we train models to predict future data values at a given position based on the past values in its neighborhood, capturing common temporal behavior in the data. We then evaluate the model's prediction on the same data. High prediction error means that the local behavior was too complex, unique or uncertain to be accurately captured during training, indicating spatiotemporal regions with interesting behavior. By training several models of varying capacity, we are able to detect spatiotemporal regions of various complexities. We aggregate the obtained prediction errors into a time series or spatial volumes and visualize them together to highlight regions of unpredictable behavior and how they differ between the models. We demonstrate two further volumetric applications: adaptive timestep selection and analysis of ensemble dissimilarity. We apply our technique to datasets from multiple application domains and demonstrate that we are able to produce meaningful results while making minimal assumptions about the underlying data.", "issn" : "2160-9306", "doi" : "10.1109/TVCG.2019.2961893", "bibtexKey": "tkachev2019local" } ] }