V2VFusion: Text-Controlled Video-to-Video Diffusion for Degradation-Aware Video Fusion

Video fusion integrates complementary information in multiple source video streams into a single sequence for complete scene perception. Existing methods perform frame-wise fusion and lack unified spatio-temporal modeling across sequences, damaging temporal coherence across sequences. When dealing with degradations, they also ignore temporal dynamics and overload a single network with diverse degradations, leading to limited and imbalanced degradation suppression. To address these issues, we propose V2VFusion, the first unified video-to-video fusion framework that directly generates temporally coherent, high-quality fused videos from degraded multi-source inputs. By reformulating video fusion as a video-to-video conditional generation problem in latent diffusion space, our approach holistically models spatio-temporal dependencies across entire sequences and intrinsically ensures sequence-level consistency. On this basis, a text-guided degradation-conditioned ControlNet bridges high-level textual degradation descriptions with low-level visual priors, enabling interpretable and controllable guidance for structure-preserving fusion beyond purely data-driven conditioning. To handle heterogeneous and compound degradations, a state-modulated degradation-aware hierarchical mixture-of-experts module decouples expert selection into diffusion-state-aware filtering and degradation-aware routing. It prevents unreliable routing and fosters expert specialization to mitigate representation conflicts among diverse degradations. Experiments on infrared-visible, multi-exposure, and multi-focus video fusion tasks and complex degradations validate that V2VFusion surpasses existing methods in fusion quality and temporal stability.