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[ICLR 2026] Continuous Space-Time Video Super-Resolution with 3D Fourier Fields

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# Continuous Space-Time Video Super-Resolution with 3D Fourier Fields

**ICLR 2026 (accepted)**

[![Page](https://img.shields.io/badge/Project-Page-green)](https://v3vsr.github.io)
[![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://arxiv.org/abs/2509.26325)
[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)

This is the official implementation of the paper
> *Continuous Space-Time Video Super-Resolution with 3D Fourier Fields* <br>
> [Alexander Becker](https://a1ex.dev),
[Julius Erbach](https://juliuserbach.github.io),
[Dominik Narnhofer](https://scholar.google.com/citations?user=tFx8AhkAAAAJ&hl=en),
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en) <br>
> ETH Zurich

## News
2026-01-26: Paper is accepted to ICLR 2026. 🎉 <br>
2025-09-30: Paper is now on [arXiv](https://arxiv.org/abs/2509.26325).<br>

## Setup

This code is tested on Linux with Python 3.11 and an NVIDIA GPU with > 20GB VRAM (e.g., {3,4,5}090 or H100). Install the required packages with
```bash
pip install -r requirements.txt
```

Then build deformable attention CUDA extensions for JAX:
```bash
cd models/rvrt_jax/deform_attn && ./build.sh && cd -
```

## Evaluate pre-trained models

Download pre-trained V3 checkpoint from [Google Drive](https://drive.google.com/file/d/15nw5NhEIf7VvetEtQI1cnrLNWPi_9FGj/view?usp=sharing) and place them in the `checkpoints/` folder.

An example command for running inference on the Vid4 dataset (x2 temporal upsampling) is:
```bash
python run_inference.py --data-dir data/ --checkpoint-path checkpoints/v3.pkl --eval-sets Vid4 --space-scale 4 --time-scale 2
```
Make sure that `--data-dir` points to a folder containing the dataset (i.e., in this case with a subfolder `Vid4`), and that `--checkpoint-path` points to a downloaded pre-trained model. For the default Adobe240 and GoPro benchmarks you likely want to use `--space-scale 4 --time-scale 8`.

We have included some logic for quickly evaluating the relevant metrics of the inference results against the ground truth, e.g.,
```bash
python eval_imgs.py data/Vid4 results/Vid4/x4 --time-scale 2
```

## Train your own model

To train on Adobe240, download the dataset from the official sources and then place videos under ./data with the following structure:

```
data/
├── Adobe240
│   ├── train
│   │   ├── ...
│   ├── valid
│   │   ├── ...
│   ├── test
│   │   ├── ...
```

Then use the convenience training script:
```bash
bash run_train.sh
```
Make sure to check `run_train.sh` and `args.py` for the availabe training options.

## Reproducibility
This branch contains the current reference implementation with the recommended initialization and training settings. Results remain consistent with those reported in the paper. For exact reproduction of the paper results, please use branch `v0.1-paper`.

## Citation
```bibtex
@inproceedings{
  becker2026continuous,
  title={Continuous Space-Time Video Super-Resolution with 3D Fourier Fields},
  author={Alexander Becker and Julius Erbach and Dominik Narnhofer and Konrad Schindler},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026}
}
```

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