Research On Immersive Visualization Of Character Relationship

Immersive visualization is an emerging research field based on the development of virtual reality and data visualization,which make full use of human perception and operational ability to support data analysis and decision making with an immersive interactable user interface.Although data visualization techniques are abundant in twodimensional space,the visualization of complicate character relationship is not so intelligible for people,and is easy to cause visual stress,which disturb data analysis.Immersive visualization situate in a three-dimensional space has an extra dimension compared to two-dimensional space,which can encode more information.In this thesis,we study the immersive character relationship visualization techniques.On the one hand,it provides reference for related research and helps people with highefficiency data analysis and decision making.On the other hand,it provides possibility for immersive visualization and enrich this research field.We firstly made a survey about virtual reality,immersive visualization and character relationship visualization to determine our research content.Then we conducted data processing with family relationship data,social relationship data and time-space geographic data according to the data characteristic.Based on these data,we designed three immersive visualization techniques with tree map to show kinship,arc diagram to visualize sophisticated social network and Geo Time visualization to present character trajectory.At last,we completed an immersive analytic system on the basis of main character relationship data in Stone Story,we designed and implemented interactive functions such as selection,filtering,rearrangement and animation controlling in immersive visualization views.On account of visual analytics in the system,users can get in-depth understanding of character relationships.The system verified the feasibility and effectiveness of the immersive visualization techniques we designed and could reduce visual burden and improve efficiency of data analysis tasks.