LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation

Min-Wu Jeong, Chae-Eun Rhee
2/10/2026

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Abstract

In this paper, we propose LC-Mamba, a Mamba-based model that captures fine-grained spatiotemporal information in video frames, addressing limitations in current interpolation methods and enhancing performance. The main contributions are as follows: First, we apply a shifted local window technique to reduce historical decay and enhance local spatial features, allowing multi-scale capture of detailed motion between frames. Second, we introduce a Hilbert curve-based selective state scan to maintain continuity across window boundaries, preserving spatial correlations both within and between windows. Third, we extend the Hilbert curve to enable voxel-level scanning to effectively capture spatiotemporal characteristics between frames. The proposed LC-Mamba achieves competitive results, with a PSNR of 36.53 dB on Vimeo-90k, outperforming prior models by +0.03 dB. The code and models are publicly available at https://github.com/Miinuuu/LCMamba.git

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Cite this paper

@article{jeong2026lcmamba,
  title  = {LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation},
  author = {Min-Wu Jeong and Chae-Eun Rhee},
  year   = {2026},
  doi    = {10.1109/CVPR52734.2025.01646},
  url    = {https://doi.org/10.1109/CVPR52734.2025.01646},
  journal = {CVPR 2025 2025}
}

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