# MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution

arXiv: https://arxiv.org/abs/2307.07988 · HTML: https://mlsanity.com/paper/2307.07988/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2023.

## Built on by (most cited first, 16 of 16)

- [Generalizable Implicit Motion Modeling for Video Frame Interpolation](https://mlsanity.com/paper/2407.08680/) (2024)
- [Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors](https://mlsanity.com/paper/2407.09919/) (2024)
- [SNeRV: Spectra-preserving Neural Representation for Video](https://mlsanity.com/paper/2501.01681/) (2025)
- [BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution](https://mlsanity.com/paper/2501.11043/) (2025)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera](https://mlsanity.com/paper/2405.13389/) (2024)
- [Space-Time Video Super-resolution with Neural Operator](https://mlsanity.com/paper/2404.06036/) (2024)
- [Continuous Space-Time Video Super-Resolution with 3D Fourier Fields](https://mlsanity.com/paper/2509.26325/) (2025)
- [Vanilla Group Equivariant Vision Transformer: Simple and Effective](https://mlsanity.com/paper/2602.08047/) (2026)
- [Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution](https://mlsanity.com/paper/2407.08466/) (2024)
- [Towards Robust and Generalizable Continuous Space-Time Video Super-Resolution with Events](https://mlsanity.com/paper/2510.03833/) (2025)
- [Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion](https://mlsanity.com/paper/2601.20308/) (2026)
- [PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction](https://mlsanity.com/paper/2605.10275/) (2026)
- [BasicAVSR: Arbitrary-Scale Video Super-Resolution via Image Priors and Enhanced Motion Compensation](https://mlsanity.com/paper/2510.26149/) (2025)
- [DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution](https://mlsanity.com/paper/2605.13182/) (2026)
- [AVSR-Diff: Scale-Agnostic Diffusion Priors for Temporally Consistent Arbitrary-Scale Video Super-Resolution](https://mlsanity.com/paper/2607.00987/) (2026)

## Abstract (arXiv)

This work addresses continuous space-time video super-resolution (C-STVSR) that aims to up-scale an input video both spatially and temporally by any scaling factors. One key challenge of C-STVSR is to propagate information temporally among the input video frames. To this end, we introduce a space-time local implicit neural function. It has the striking feature of learning forward motion for a continuum of pixels. We motivate the use of forward motion from the perspective of learning individual motion trajectories, as opposed to learning a mixture of motion trajectories with backward motion. To ease motion interpolation, we encode sparsely sampled forward motion extracted from the input video as the contextual input. Along with a reliability-aware splatting and decoding scheme, our framework, termed MoTIF, achieves the state-of-the-art performance on C-STVSR. The source code of MoTIF is available at https://github.com/sichun233746/MoTIF.