---
license: cc-by-nc-4.0
tags:
  - change-detection
  - image-registration
  - optical-flow
  - diffusion
  - image-morphing
  - remote-sensing
library_name: pytorch
---

# Morphing Through Time — Pretrained Weights

Weights for **Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust
Alignment in Change Detection** (Madani & Patel).

- 📄 Paper: https://arxiv.org/abs/2511.07976
- 💻 Code: https://github.com/Anita-Madani/Morphing-through-time-

<p align="center">
  <img src="pipeline.png" width="100%" alt="Morphing Through Time pipeline">
</p>

Given a bi-temporal pair `(I_A, I_B)`, DiffMorpher synthesizes `K=5` intermediate frames;
RoMa estimates the flow between consecutive frames, composed into `F_{A→B}`; a residual
flow-refinement U-Net corrects it to `F̂_{A→B}`, which warps `I_B` onto `I_A` before the
(frozen) change-detection backbone.

## Contents

This repository hosts the trained **Stage-3 residual-refiner** checkpoints
(`<dataset>/refiner.pth` for LEVIR / WHU / DSIFN). The diffusion backbone
(Stable Diffusion 2.1) and RoMa weights download automatically on first use, so they are
not stored here.

```bash
pip install -U huggingface_hub
bash scripts/download_weights.sh    # from the code repo; pulls checkpoints into ./checkpoints/
```

## License

Non-commercial research use (CC BY-NC 4.0). The morphing stage is derived from DiffMorpher
under the S-Lab License 1.0; see the code repository for details.

## Citation

```bibtex
@article{madani2025morphing,
  title   = {Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection},
  author  = {Madani, Seyedehanita and Patel, Vishal M.},
  journal = {arXiv preprint arXiv:2511.07976},
  year    = {2025}
}
```