# AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

arXiv: https://arxiv.org/abs/2005.01233 · HTML: https://mlsanity.com/paper/2005.01233/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 3 of 3)

- [NTIRE 2021 Challenge on Video Super-Resolution](https://mlsanity.com/paper/2104.14852/) (2021)
- [Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New Dataset and Temporal Unfolding](https://mlsanity.com/paper/2303.15417/) (2023)
- [Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning](https://mlsanity.com/paper/2406.05974/) (2024)

## Abstract (arXiv)

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results. From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-framerate (60 fps) video sequences by estimating temporally intermediate frames. We employ the REDS VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. The competition had 62 registered participants, and a total of 8 teams competed in the final testing phase. The challenge winning methods achieve the state-of-the-art in video temporal superresolution.