Zero-Shot infrared-guided HDR video deflickering

High dynamic range (HDR) imaging is crucial for realistic lighting, but image-trained methods often flicker on video due to the absence of temporal consistency constraints, especially for infrared-guided HDR, where aligning HDR and IR video data is challenging. This paper proposes ZS-HDRTVNet, a zero-shot framework that achieves temporally stable IR-guided HDR video without aligned IR–HDR videos for training. A channel-aligned fusion (CAF) module maps RGB/IR features into orthogonal spaces for simple addition-based fusion and operates flexibly with or without IR. A temporal consistency branch couples optical-flow estimation with occlusion handling to model inter-frame motion and suppress flicker. We train CAF on aligned IR–HDR images and the temporal branch on HDR videos without IR, enabling complementary supervision. Experimental results demonstrate that the proposed method achieves state-of-the-art HDR quality and temporal consistency, effectively eliminating flicker and enhancing visual coherence.