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-
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.
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
@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}
}