DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution

• URL: http://arxiv.org/abs/2605.13182v1
• Date: Wed, 13 May 2026 08:41:48 GMT
• Title: DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
• Abstract summary: We introduce DiffST, an efficienttemporal-aware video diffusion framework for real-world video super-resolution (STVSR)<n>To improve efficiency, we adapt a pre-trained diffusion model for one-step sampling and process the entire video directly rather than operating on individual frames.<n>Experiments show that DiffST obtains leading results on real-world STVSR tasks.
• Score: 38.348149648031885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based models have shown strong performance in video super-resolution (VSR) and video frame interpolation (VFI). However, their role in the coupled space-time video super-resolution (STVSR) setting remains limited. Existing diffusion-based STVSR approaches suffer from two issues: (1) low inference efficiency and (2) insufficient utilization of spatiotemporal information. These limitations impede deployment. To address these issues, we introduce DiffST, an efficient spatiotemporal-aware video diffusion framework for real-world STVSR. To improve efficiency, we adapt a pre-trained diffusion model for one-step sampling and process the entire video directly rather than operating on individual frames. Furthermore, to enhance spatiotemporal information utilization, we introduce cross-frame context aggregation (CFCA) and video representation guidance (VRG). The CFCA module aggregates information across multiple keyframes to produce intermediate frames. The VRG module extracts video-level global features to guide the diffusion process. Extensive experiments show that DiffST obtains leading results on real-world STVSR tasks. It also maintains high inference efficiency, running about 17$\times$ faster than previous diffusion-based STVSR methods. Code is available at: https://github.com/zhengchen1999/DiffST.

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