Improve dataset card: add task category and paper link

README.md

@@ -1,290 +1,37 @@
 ---
-base_model:
-- Wan-AI/Wan2.1-VACE-1.3B
 license: apache-2.0
-pipeline_tag: video-to-video
-library_name: diffusers
+task_categories:
+- image-to-image
 ---
 
-<div align="center">
-    <h1>
-    SVOR (<b>S</b>table <b>V</b>ideo <b>O</b>bject <b>R</b>emoval)
-    </h1>
-    <p>
-    Official PyTorch code for <em>From Ideal to Real: Stable Video Object Removal under Imperfect Conditions</em><br>    </p>
-    </p>
-    <a href="https://arxiv.org/abs/2603.09283"><img src="https://img.shields.io/badge/arXiv-2603.09283-b31b1b" alt="version"></a>
-    <a href="https://xiaomi-research.github.io/svor" target='_blank'>
-        <img src="https://img.shields.io/badge/🐳-Project%20Page-blue">
-    </a>
-    <a href='https://github.com/xiaomi-research/svor/'>
-        <img src='https://img.shields.io/badge/github-code-blue?logo=github'>
-    </a>
-    <a href='https://huggingface.co/datasets/HigherHu/RORD-50'>
-        <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-RORD--50-orange'>
-    </a>
-    <!-- <a href="https://huggingface.co/spaces/xiaomi/SVOR" target='_blank'>
-        <img src="https://img.shields.io/badge/Demo-%F0%9F%A4%97%20Hugging%20Face-blue">
-    </a> -->
-    <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="mit"></a>
+# RORD-50 Dataset
 
+This repository contains the **RORD-50** dataset, introduced in the paper [From Ideal to Real: Stable Video Object Removal under Imperfect Conditions](https://huggingface.co/papers/2603.09283).
 
-⭐ If SVOR is helpful to your projects, please help star this repo. Thanks! 🤗
+[**Project Page**](https://xiaomi-research.github.io/svor/) | [**GitHub**](https://github.com/xiaomi-research/svor) | [**Paper**](https://huggingface.co/papers/2603.09283)
 
-</div>
+## Introduction
+The RORD-50 dataset is a benchmark designed to evaluate video object removal performance under real-world challenges, such as shadows, abrupt motion, and defective masks. It was introduced as part of the **Stable Video Object Removal (SVOR)** framework, which focuses on achieving shadow-free, flicker-free, and mask-defect-tolerant removal.
 
 ## Overview
-
-![overall_structure](asset/framework.png)
-
-Removing objects from videos remains difficult in the presence of real-world imperfections such as shadows, abrupt motion, and defective masks. Existing diffusion-based video inpainting models often struggle to maintain temporal stability and visual consistency under these challenges. We propose **Stable Video Object Removal (SVOR)**, a robust framework that achieves shadow-free, flicker-free, and mask-defect-tolerant removal through three key designs: (1) **Mask Union for Stable Erasure (MUSE)**, a windowed union strategy applied during temporal mask downsampling to preserve all target regions observed within each window, effectively handling abrupt motion and reducing missed removals; (2) **Denoising-Aware Segmentation (DA-Seg)**, a lightweight segmentation head on a decoupled side branch equipped with {Denoising-Aware AdaLN } and trained with mask degradation to provide an internal diffusion-aware localization prior without affecting content generation; and (3) **Curriculum Two-Stage Training**: where Stage I performs self-supervised pretraining on unpaired real-background videos with online random masks to learn realistic background and temporal priors, and Stage II refines on synthetic pairs using mask degradation and side-effect-weighted losses, jointly removing objects and their associated shadows/reflections while improving cross-domain robustness. Extensive experiments show that SVOR attains new state-of-the-art results across multiple datasets and degraded-mask benchmarks, advancing video object removal from ideal settings toward real-world applications.
-
-## Results
-
-For more visual results, go checkout our <a href="https://xiaomi-research.github.io/svor/" target="_blank">project page</a>
-
-<h3>Common Masks</h3>
-<table>
-  <thead>
-    <tr>
-      <th>Masked Input</th>
-      <th>Result</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input/bmx-bumps.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/input/bmx-bumps.gif" width="100%">
-      </td>
-	  <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result/bmx-bumps.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/result/bmx-bumps.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input/boat.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/input/boat.gif" width="100%">
-      </td>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result/boat.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/result/boat.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input/bus.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/input/bus.gif" width="100%">
-      </td>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result/bus.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/result/bus.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input/varanus-cage.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/input/varanus-cage.gif" width="100%">
-      </td>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result/varanus-cage.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/result/varanus-cage.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-  </tbody>
-</table>
-
-<h3>Defective Masks</h3>
-<table>
-  <thead>
-    <tr>
-      <th>Masked Input</th>
-      <th>Result</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input_maskdrop0.5/camel.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/input_maskdrop0.5/camel.gif" width="100%">
-      </td>
-	  <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result_maskdrop0.5/camel.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/result_maskdrop0.5/camel.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input_maskdrop0.5/dog-gooses.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/input_maskdrop0.5/dog-gooses.gif" width="100%">
-      </td>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result_maskdrop0.5/dog-gooses.mp4" type="video/mp4"> Your browser does not support the video tag. </video> -->
-        <img src="asset/examples/result_maskdrop0.5/dog-gooses.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input_maskdrop0.5/elephant.mp4" type="video/mp4"> Your browser does not support the video tag. </video>  -->
-        <img src="asset/examples/input_maskdrop0.5/elephant.gif" width="100%">
-      </td>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result_maskdrop0.5/elephant.mp4" type="video/mp4"> Your browser does not support the video tag. </video>  -->
-        <img src="asset/examples/result_maskdrop0.5/elephant.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/input_maskdrop0.5/kite-walk.mp4" type="video/mp4"> Your browser does not support the video tag. </video>  -->
-        <img src="asset/examples/input_maskdrop0.5/kite-walk.gif" width="100%">
-      </td>
-      <td>
-        <!-- <video width="480" autoplay loop muted playsinline controls> <source src="asset/examples/result_maskdrop0.5/kite-walk.mp4" type="video/mp4"> Your browser does not support the video tag. </video>  -->
-        <img src="asset/examples/result_maskdrop0.5/kite-walk.gif" width="100%">
-      </td>
-    </tr>
-    <tr>
-  </tbody>
-</table>
-
-## Dependencies and Installation
-
-The code is tested with Python 3.10
-
-1. Clone Repo
-
-   ```bash
-   git clone https://github.com/xiaomi-research/SVOR.git
-   ```
-2. Create Conda Environment and Install Dependencies
-
-   ```bash
-   # create new anaconda env
-   conda create -n svor python=3.10 -y
-   conda activate svor
-
-   # install pytorch and xformers
-   pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 xformers==0.0.30
-
-   # install other python dependencies
-   pip install -r requirements.txt
-   ```
-3. [Optional] Install flash-attn, refer to [flash-attention](https://github.com/Dao-AILab/flash-attention)
-
-    ```bash
-    pip install packaging ninja psutil
-    pip install flash-attn==2.7.4.post1 --no-build-isolation
-    ```
-
-### [Optional] Run with docker
-
-```bash
-docker build -f Dockerfile.ds -t SVOR:latest .
-docker run --gpus all -it --rm -v /path/to/videos:/data -v /path/to/models:/root/models SVOR:latest
-```
-
-## Pretrained Weights
-
-Download pretrained weights and put them to `models/`:
-
-- download [Wan-AI/Wan2.1-VACE-1.3B](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B)
-- download our trained two loras from [HigherHu/SVOR](https://huggingface.co/HigherHu/SVOR)
-
-The files in `models/` are as follows:
-
-```
-
-models/
-├── put models here.txt
-├── remove_model_stage1.safetensors
-├── remove_model_stage2.safetensors
-└── Wan2.1-VACE-1.3B/
-
-```
-
-## Quick test
-
-Run the following scripts, and results will be save to `samples/SVOR/`:
-
-```python
-python predict_SVOR.py \
-  --input_video samples/input/bmx-bumps_raw.mp4 \
-  --input_mask_video samples/input/bmx-bumps_mask.mp4
-```
-
-```
-Usage:
-
-python predict_SVOR.py [options]
-
-Some key options:
-  --input_video            Path to input video
-  --input_mask_video       Path to mask video
-  --num_inference_steps    Inference steps (default: 20)
-  --save_dir               Output directory
-  --sample_size            Frame size: height width (default: 720 1280)
-```
-
-ATTENTION: It will need no less than **40GB** GPU memory to run the inference.
-
-## Interactive Demo
-
-1. Install [SAM2](https://github.com/facebookresearch/sam2) and download pretrained weights [sam2.1_hiera_large.pt](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt) to `models/`
-
-2. Start the gradio demo
-
-    ```bash
-    python -m demo.gradio_app
-    ```
-
-    Ensure it print the following informations:
-    ```
-    ...
-    [Info] SAM2 Predictor initialized successfully
-    ...
-    [Info] Removal model Predictor initialized successfully
-    Running on local URL:  http://0.0.0.0:7861
-
-    ```
-
-3. Open the web page: http://[ServerIP]:7861
-
-    ```
-    Usage
-    1. Upload a video and click "Process video" button in the "1. Upload and Preprocess" tab page
-    2. Switch to "2. Annotate and Propagate" tab page, click to segment the objects
-    3. "Add annotation" and "Propagate masks", to finish the segmentation
-    4. Check the object ID in "Display object list", and switch to "3. Remove Objects" tab page
-    5. Click "Preview video" to preview input video and mask video
-    6. Click "Start removal" to run the SVOR algorithm
-    ```
-
-## RORD-50 Dataset
-
-The RORD-50 Dataset can be downloaded from [TBD](TBD)
-
-
-## Acknowledgement
-
-Our work benefit from the following open-source projects:
-
-- [VideoX-Fun](https://github.com/aigc-apps/VideoX-Fun)
-- [VACE](https://github.com/ali-vilab/VACE)
-- [ROSE](https://github.com/Kunbyte-AI/ROSE)
-- [SAM2 - Segment Anything Model 2](https://github.com/facebookresearch/sam2)
-- [RORD](https://github.com/Forty-lock/RORD)
+Removing objects from videos remains difficult in the presence of real-world imperfections. SVOR advances video object removal from ideal settings toward real-world applications by handling abrupt motion and mask defects effectively. This dataset provides the necessary benchmarks for testing the robustness and temporal stability of video inpainting models.
 
 ## Citation
-
-If you find our repo useful for your research, please consider citing our paper:
+If you find this dataset or the SVOR framework useful for your research, please consider citing the paper:
 
 ```bibtex
 @article{hu2026svor,
    title={From Ideal to Real: Stable Video Object Removal under Imperfect Conditions},
-   author={Hu, Jiagao and Chen, Yuxuan and Li, Fuhao and Wang, Zepeng and Wang, Fei and Zhou, Daiguo and Luan, Jian},
+   author={Hu, Jiagao and Chen, Yuxuan and Li, Fuhao and Wang, Zepeng and Wang, Fei and Daiguo, Zhou and Luan, Jian},
    journal={arXiv preprint arXiv:2603.09283},
    year={2026}
 }
 ```
+
+## Acknowledgement
+This work benefits from the following open-source projects:
+- [VideoX-Fun](https://github.com/aigc-apps/VideoX-Fun)
+- [VACE](https://github.com/ali-vilab/VACE)
+- [ROSE](https://github.com/Kunbyte-AI/ROSE)
+- [SAM2 - Segment Anything Model 2](https://github.com/facebookresearch/sam2)
+- [RORD](https://github.com/Forty-lock/RORD)