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Spatial information understanding is fundamental to visual perception in metaverse. 

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustsson et al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods. 

Abstract: Navigation is a major challenge in exploring data within immersive environments, especially of large omnidirectional spherical images. We propose a method of auto-scaling to allow users to navigate using teleportation within the safe boundary of their physical environment with different levels of focus. Our method combines physical navigation with virtual teleportation. We also propose a “peek then warp” behavior when using a zoom lens and evaluate our system in conjunction with different teleportation transitions, including a proposed transition for exploration of omnidirectional and 360-degree panoramic imagery, termed Envelop, wherein the destination view expands out from the zoom lens to completely envelop the user. In this work, we focus on visualizing and navigating large omnidirectional or panoramic images with application to GIS visualization as an inside-out omnidirectional image of the earth. We conducted two user studies to evaluate our techniques over a search and comparison task. Our results illustrate the advantages of our techniques for navigation and exploration of omnidirectional images in an immersive environment.