Omnidirectional capture of dynamic scenes facilitates the creation of immersive virtual reality assets and holistic scene understanding. Outward-facing multi-fisheye camera rigs offer an efficient solution for full-scene coverage, using fewer lenses than conventional pinhole arrays while enabling all-directional observation of complex, time-varying environments. By continuously recording scene evolution from every angle, these systems naturally enable a richer characterization of dynamic interactions. Despite these advantages, dynamic scene modeling in this setting remains underexplored. Existing methods, typically designed for fixed pinhole configurations or monocular setups, rely heavily on photometric cues and often neglect the strong geometric and semantic priors inherent in multi-fisheye omnidirectional data. To address this gap, we present OmniPrior, a Gaussian Splatting-based framework for outward-facing, multi-fisheye omnidirectional capture. Our approach incorporates metric-geometry-aware initialization with multi-prior guidance, introducing a dynamicness-aware Gaussian representation that encodes both object motion and subtle temporal variations. The resulting representations are physically consistent and temporally stable. Extensive experiments validate the effectiveness of our method in novel view synthesis across new viewpoints and timestamps. We demonstrate its utility in two representative applications derived from our learned representations: 6DoF rendering with flexible FoV and motion-freeze rendering.

Consumer-level omnidirectional video offers an economically viable means to create virtual reality (VR) assets, enabling users to explore and interact within a fully immersive visual environment. However, editing such videos, particularly those with 360${}^{\circ }$∘ views and dynamic objects, poses significant challenges. Existing approaches to representing and manipulating omnidirectional content-whether designed for typical 2D perspective imagery or panoramas-often fail to adequately capture the complex spatiotemporal relationships crucial for producing high-quality, editable outputs in dynamic, panoramic settings. To overcome these challenges, we introduce OmniPlane, a novel method that leverages spherical spatiotemporal feature grids to empower the representation and editability of real-world dynamic omnidirectional environments casually captured by commodity omnidirectional cameras. OmniPlane computes spatiotemporal features by fusing vectors or matrices from each learnable spatial and spatiotemporal feature plane within a spherical coordinate system, complemented by a specifically designed weighted sampling strategy respecting the inherent spherical distribution of omnidirectional content. These learned feature planes can be flexibly decomposed into palette-based color bases. This innovative method not only enhances the representation capability of omnidirectional content and dynamics but also enables the recoloring of omnidirectional videos. Extensive experiments and a dedicated user study validate the superior performance of our proposed method in facilitating recolorable representations of dynamic omnidirectional environments.