HieraVisVR: Hierarchical Visual Analytics for Motion-Centric VR Playtesting
An ACM CHI 2026 paper on hierarchical visual analytics for motion-centric VR playtesting, focused on helping researchers and developers better inspect playtest behavior in immersive environments.
Publication details
Abstract
Playtesting is widely used in the game industry to identify design flaws and evaluate player experience, yet little research explores how to effectively visualize and analyze playtesting data. This challenge is particularly pronounced in motion-based VR games, which involve physical movements and interactions tracked through multimodal inputs, resulting in complex multidimensional data. To better understand the challenges designers face, we conducted a formative study with 30 practitioners in the VR domain to characterize playtesting workflows and associated tasks. Based on these findings, we present HieraVisVR, a hierarchical visual analytics framework that incorporates body-motion-related data to help designers identify player behaviors and critical game moments, simplifying their workflow. We demonstrate the applicability of HieraVisVR in three different applications and evaluate our system with playtesting experts through an analysis of motion-based game data. The study results suggest that our system enhances playtesters' understanding of the gameplay and improves their data analysis workflow.