ScopeVSR: A Broad-Coverage Training Dataset for Real-World Video Super-Resolution

Diffusion models have shown superior performance in video super-resolution (VSR). However, restoring diverse real-world content remains challenging due to the lack of balanced semantic coverage in existing training data. In this paper, we introduce a structured curation pipeline for open-domain VSR, SCOPE. In this pipeline, we combine task-oriented video selection with hierarchical semantic rebalancing. Applying this pipeline to a large-scale collection of data sources, we construct the SCOPE-VSR dataset for training and SCOPE-test for testing. SCOPE-VSR has approximately 27k videos with a balanced and diverse coverage of various real-world scenarios and subjects. In addition, we propose GraCo-VSR, a novel framework for one-step video restoration. GraCo-VSR adopts a reliability- aware design with explicit supervision to reduce degradation-induced distortions in motion and texture guidance. This design enables the model to better leverage the diverse training content in SCOPE-VSR, thereby supporting generalization across varied scenarios. Experiments demonstrate that our model exhibits strong performance on both perceptual fidelity and visual quality.