Blind Omnidirectional Video Super-Resolution via Geometry-Conditioned Probabilistic Degradation Modeling

Blind Video Super-Resolution (BVSR) promotes generalization across diverse imaging conditions by explicitly accounting for unknown degradations to guide reconstruction. However, its extension to OmniDirectional Video (ODV) remains underexplored. The unique fisheye imaging and EquiRectangular Projection (ERP) process of ODVs couples unknown capture degradations with projection geometry, making local degradation characteristics jointly dependent on both factors and complicating degradation inference. Therefore, we propose OmniVaria, the first blind ODVSR framework with a particular focus on modeling the interaction between unknown degradations and spherical geometry. In particular, we introduce Probabilistic Spherical Degradation Conditioning (PSDC), which leverages explicit spherical geometry as an additional condition in probabilistic degradation modeling, i.e., representing unknown degradations through geometrically conditioned multivariate Gaussian distributions in a latent space, with posterior statistics inferred from Low-Resolution (LR) ODV observations guiding reconstruction. For PSDC optimization, we establish an information-theoretically grounded regularization term, named Geometry-Conditioned Variational Information Bottleneck (GCVIB), and derive its tractable variational form for practical optimization. Drawing on information bottleneck theory, GCVIB encourages PSDC to learn a minimally sufficient representation of LR observations for High-Resolution (HR) reconstruction, thereby promoting generalization to unseen scenes and degradation levels. Furthermore, recognizing that ERP projection transforms coherent scene motion into spatially varying displacements and deformations, complicating the exploitation of temporal context, we bypass explicit motion estimation and develop Implicit Omnidirectional Temporal Aggregation (IOTA), which jointly processes current-frame features and propagated hidden states to exploit inter-frame correlations for reconstruction. Extensive experiments on various ODV benchmarks and degradation settings demonstrate the effectiveness of our design. The code will be publicly released.