Add pipeline tag, library name, and improve model card

README.md

@@ -1,19 +1,24 @@
 ---
-license: mit
 base_model:
 - stabilityai/stable-diffusion-2
+license: mit
+library_name: diffusers
+pipeline_tag: image-to-image
 ---
+
 # StableMotion: Repurposing Diffusion-Based Image Priors for Motion Estimation
 This is the official repo for paper [StableMotion: Repurposing Diffusion-Based Image Priors for Motion Estimation](https://www.arxiv.org/abs/2505.06668)
 
+Official Code Repository: [GitHub - ivowang/StableMotion](https://github.com/ivowang/StableMotion)
+
 ## Setup
-0. Clone the [code repo](https://github.com/ziyiwhat/StableMotion).
+0. Clone the [code repo](https://github.com/ivowang/StableMotion).
 1. Create your environment from `requirements.txt`.
 2. Download [DIR-D](https://drive.google.com/file/d/1KR5DtekPJin3bmQPlTGP4wbM1zFR80ak/view?usp=sharing) and [RS-Real](https://huggingface.co/datasets/Yzl-code/RS-Diffusion/tree/main). Put them into `StableMotion_SIR` and `StableMotion_RSC` respectively.
 
 ## StableMotion for Stitched Image Rectangling (SIR)
 ### Inference
-0. Download the checkpoints of `StableMotion_SIR`
+0. Download the checkpoints of [StableMotion_SIR](https://huggingface.co/ziyiwhat/StableMotion/tree/main/StableMotion_SIR)
 1. Run `cd StableMotion_SIR && sh sample.sh`. You may want to change this file to modify the inference configurations.
 2. Run `sh metrics.sh` to evaluate the results.
 
@@ -23,9 +28,23 @@ This is the official repo for paper [StableMotion: Repurposing Diffusion-Based I
 
 ## StableMotion for Rolling Shutter Correction (RSC)
 ### Inference
-0. Download the checkpoints of `StableMotion_RSC`
+0. Download the checkpoints of [StableMotion_RSC](https://huggingface.co/ziyiwhat/StableMotion/tree/main/StableMotion_RSC)
 1. Run `cd StableMotion_RSC && sh sample.sh`. You may want to change this file to modify the inference configurations.
 2. Run `sh metrics.sh` to evaluate the results.
 
 ### Training
-Run `cd StableMotion_RSC && sh train.sh`. You may want to change this file to modify the training configurations. The default configuration requires approximately 40 GB of VRAM per card.
+Run `cd StableMotion_RSC && sh train.sh`. You may want to change this file to modify the training configurations. The default configuration requires approximately 40 GB of VRAM per card.
+
+## GPT Rule-Based Evaluation
+Each task folder has a `gpt_eval` subfolder with the script used in the paper to score results with a vision LLM (GPT) on a fixed rubric. `StableMotion_SIR/gpt_eval/score_rectangle.py` scores Stitched Image Rectangling (SIR) `(input, output)` pairs, and `StableMotion_RSC/gpt_eval/score_rolling_shutter.py` scores Rolling Shutter Correction (RSC) `[input | Yang | Ours]` triptychs. Both call an OpenAI-Responses-compatible API and emit per-pair scores plus an aggregate `summary.json` (mean/std/95% CI). To run, copy `provider.example.json` to `provider.json` in the relevant folder, add your endpoint/key, then e.g. `cd StableMotion_SIR/gpt_eval && python score_rectangle.py <input_dir> <result_dir>`. See each folder's `README.md` for the full rubric, flags, and outputs.
+
+## Citation
+
+```bibtex
+@article{wang2025stablemotion,
+  title={StableMotion: One-Step Motion Estimation with Diffusion Prior},
+  author={Wang, Ziyi and Li, Haipeng and Sui, Lin and Zhou, Tianhao and Jiang, Hai and Nie, Lang and Liu, Shuaicheng},
+  journal={arXiv preprint arXiv:2505.06668},
+  year={2025}
+}
+```