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

Zheng Chen, Ruofan Yang, Jin Han, Dehua Song, Zichen Zou, Chunming He, Yong Guo, Yulun Zhang, "DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution"

[project] [arXiv]

🔥🔥🔥 News

• 2026-05-13: This repo is released. DiffST is available on arXiv.

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× faster than previous diffusion-based STVSR methods.

⚒️ TODO

• Release code and pretrained models

🔎 Method Overview

🔎 Results

@article{chen2026diffst,
  title = {DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution},
  author = {Chen, Zheng and Yang, Ruofan and Han, Jin and Song, Dehua and Zou, Zichen and He, Chunming and Guo, Yong and Zhang, Yulun},
  journal = {arXiv preprint arXiv:2605.13182},
  year = {2026}
}