| --- |
| 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} |
| } |
| ``` |
|
|
