LiveVSR: Reviving Live Photos via Cover-Guided Video Super-Resolution

Live Photo pairs a high-quality cover image with a lower-quality video of the same moment. Existing cover-guided methods mainly enhance individual frames, while complete video restoration has received less attention. Two challenges hinder this task: uncertain cover-video correspondence and residual degradation in the cover. To address these challenges, we propose LiveVSR, a cover-guided one-step diffusion framework for complete Live Photo super-resolution. We combine continuous reference features with a discrete visual prior to better use the cover. First, we build an in-context framework to jointly model cover and video tokens through bidirectional attention. Flow-guided local retrieval combines motion cues with content-based reference matching. Meanwhile, we introduce correspondence-guided codebook injection. A codebook pretrained on high-quality images provides discrete cover features. These features are transferred through contextualized correspondences to complement continuous reference guidance. Training with clean and mildly degraded covers further accounts for reference imperfections. We compare the proposed LiveVSR with conventional VSR, reference-guided VSR, and Live Photo image enhancement methods. Experiments on synthetic benchmarks and real Live Photos demonstrate favorable visual quality, reconstruction fidelity, and temporal consistency. Code will be released.