Whatever Could Be, Could Be: Visualizing Future Movement Predictions
2024 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES, VR 2024(2024)
Univ South Australia
Abstract
As technology grants us superhuman powers, looking into what the future may hold is no longer science fiction. Artificial Intelligence and Mixed Reality technologies can allow users to see what the future may hold. In this paper, we present our work evaluating visualizations of future predictions in the Football domain. We explore the problem space, examining what a future may be. Three visualizations-2 Arrow Lines, 5 Arrow Lines, and Heatmap-are introduced as representations that show both individual predictions of movement (2 Arrow Lines and 5 Arrow Lines) and more generalized predictions (Heatmap). Whilst football is used as an example domain in this work, the visualizations and findings aim to generalize to other scenarios that contain trajectory information. Two VR studies (2 x n = 24) examined the visualizations in both simple/complex, timed/non-timed, and short/long-range viewing situations. Results show Heatmap as the most effective and preferred by the vast majority of participants. Findings offer insights into future visualization, serving as visual heuristics beyond the realm of sports and into the real world.
MoreTranslated text
Key words
Human-centered computing-VisualizationVisualization techniques-Future visualizations,Human-centered computing-Visualization-Visualization design and evaluation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined