## 4 Experimental Results
### Panoramic Generative Tales  
This set of scenes (short immersive tales), including visual, aural and narrative content, was generated by a single prompt through multiple AI services. Such a prompt was used to generate the immersive panoramic environment, using Blockade Labs' Skybox AI service [[https://www.blockadelabs.com/](https://www.blockadelabs.com/)]([https://www.blockadelabs.com/](https://www.blockadelabs.com/)) (accessed on 22 April 2024). The same prompt was used in ChatGPT to generate a short tale; the resulting text was also used in a few text-to-speech AI services to create aural content suitable for immersive VR consumption (see Figure 7). The overall time required to generate the two immersive 3D scenes was about 15 min. For panoramic scenes specifically (equirectangular data), we focused our analysis only on view direction attributes (\(V\)), since 360 content in ATON is assumed with an \"infinite\" distance; thus, local variations in \(P\) were not useful for our study. The Merkhet tool automatically detects whether the scene presents pure panoramic content, adjusting  
Figure 7: The two generative tales: “The Two Houses” (**left**) and “The Floating Village” (**right**), with welcome popups introducing the story (**top**) while a voice narrates the immersive panoramic content (using a HMD). how the visual trajectories are computed and rendered--in this case, they were re-projected using \(V\) attributes (see Figure 8A,B). After the processing step on the collected raw data (see Section 3.2), Merkhet was used to remotely access, inspect and annotate the sessions and data aggregates (Section 3.3. Standard visualization allowed us to load single or multiple records (see Figure 8A) to analyze the view direction trajectories of the ArcheoVirtual participants. Figure 8B shows an immersive inspection (Meta Quest 2) of one record from TourismA, with a bookmark on the house entrance from another analyst. As described in Section 3.3, Merkhet allows one to locally compute basic aggregates, such as location fixations (Figure 8C-F), that can be inspected through bot-immersive or non-immersive nodes. It was thus possible to inspect single records and compute fixations directly inside the browser, using voxel-based routines (each computation assumes a 3D space). Through the tool, it was possible to compare also sessions from the two different exhibits, as well as data aggregates. Two view direction KDEs were computed per exhibit and compared through the tool (Figure 8G,H). The results show only a few differences between the respective visitors, although they all seemed to focus mainly on one of the two houses narrated in the story. To determine whether the users clustered in different groups according to the trajectories of their view direction attributes, \(V\), in the \"The Two Houses\" panoramic view, we implemented an agglomerative clustering model (Figure 9). Let a view direction trajectory be a sequence of view directions \(\mathbf{V}_{u}=(V_{1},V_{2},\dots V_{n_{u}})\). To compute the agglomerative clustering model, we selected the Pearson correlation between pairs of view trajectory 2D KDEs as the metric. Thus, we computed the view direction KDEs, \(\hat{f}_{e}(V|\mathbf{V}_{u})\), for each trajectory \(\mathbf{V}_{u}\), with Equation (1), and used [PERSON]'s rule [51] for bandwidth selection. Finally, we computed the Pearson correlation between the view trajectory KDE couples. Figure 8: Data records and aggregates from ArcheoVirtual (**left**) and TourismA (**right**). (**A**) Reprojected view trajectories into the panoramic space; (**B**) analyst using VR headset to inspect a TourismA session, with a bookmark created by another analyst; (**C–F**) per-session locally computed fixations and immersive inspection (**F**); (**G,H**) server-side computed KDE for view directions. Figure 9: Agglomerative clustering of “The Two Houses” users’ view trajectories from ArcheoVirtual. View trajectories are sequences of users’ \(V\) recorded with a 0.2 s sample time.