Update README.md

README.md

@@ -48,19 +48,19 @@ tags:
 </div>
 
 <div>
-  <a href="https://zhao0100.github.io/RevealLayer/" target="_blank">
+  <a href="https://github.com/360CVGroup/RevealLayer/" target="_blank">
     <img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages">
   </a>
    
-  <a href="TODO_ARXIV_LINK" target="_blank">
+  <a href="https://arxiv.org/abs/2605.11818" target="_blank">
     <img src="https://img.shields.io/static/v1?label=Paper&message=arXiv&color=red&logo=arxiv">
   </a>
    
-  <a href="TODO_DATASET_LINK" target="_blank">
+  <a href="https://huggingface.co/qihoo360/RevealLayer" target="_blank">
     <img src="https://img.shields.io/static/v1?label=Dataset&message=RevealLayer&color=green">
   </a>
    
-  <a href="TODO_MODEL_LINK" target="_blank">
+  <a href="https://huggingface.co/qihoo360/RevealLayer" target="_blank">
     <img src="https://img.shields.io/static/v1?label=Model&message=HuggingFace&color=yellow">
   </a>
 </div>
@@ -77,7 +77,7 @@ RevealLayer decomposes an RGB image into multiple RGBA layers, enabling precise
     <img style="width:100%" src="assets/demo1.png" alt="RevealLayer teaser">
 </div>
 
-For more visual results, go checkout our <a href="https://zhao0100.github.io/RevealLayer/" target="_blank">project page</a>.
+For more visual results, go checkout our <a href="https://360cvgroup.github.io/RevealLayer/" target="_blank">project page</a>.
 
 ---
 
@@ -85,9 +85,9 @@ For more visual results, go checkout our <a href="https://zhao0100.github.io/Rev
 
 ## ⭐ Update
 
-- **[2026.05]** We released the RevealLayer paper and inference code.
-- **[2026.05]** We released the RevealLayer checkpoint.
 - **[2026.05]** RevealLayer has been accepted by ICML 2026.
+- **[2026.05]** We released the RevealLayer paper and inference code.
+- **[2026.05]** We released the RevealLayer checkpoint on [Hugging Face](https://huggingface.co/qihoo360/RevealLayer).
 
 ### ✅ TODO
 
@@ -114,133 +114,10 @@ RevealLayer focuses on occlusion-aware image layer decomposition, recovering vis
 
 We construct a large-scale multi-layer image decomposition dataset, including **RevealLayer-100K** for training and **RevealLayerBench** for evaluation. RevealLayer-100K contains 100K multi-layer natural image tuples with RGB images, background layers, RGBA foreground layers, and bounding boxes. RevealLayerBench contains 200 high-quality manually curated images, covering challenging cases such as complex occlusions, large-area objects, transparent materials, small foreground objects, and multi-layer scenes.
 
-🔥 We will release **RevealLayer-100K** and **RevealLayerBench** on [Hugging Face](TODO_DATASET_LINK). We hope they can serve as useful training and evaluation resources for future research on occlusion-aware image layer decomposition.
+🔥 We will release **RevealLayer-100K** and **RevealLayerBench** on [Hugging Face](https://huggingface.co/qihoo360/RevealLayer). We hope they can serve as useful training and evaluation resources for future research on occlusion-aware image layer decomposition.
 
 > 🚩 The datasets are intended for research use. Please follow the license and terms provided with the released dataset.
 
----
-
-## 🔧 Quick Start
-
-### 0. Experimental environment
-
-We tested our inference code with Python 3.10 and CUDA GPUs.
-
-### 1. Setup repository and environment
-
-```bash
-git clone https://github.com/Zhao0100/RevealLayer.git
-cd RevealLayer
-
-conda create -n reveallayer python=3.10
-conda activate reveallayer
-
-pip install -r requirements.txt
-
-pip install flash-attn --no-build-isolation
-
-cd diffusers
-pip install .
-cd ..
-```
-
----
-
-## 📦 Prepare the models
-
-Model files are hosted with Git LFS, so please enable Git LFS before cloning model repositories.
-
-```bash
-git lfs install
-```
-
-Download the RevealLayer checkpoint:
-
-```bash
-git clone https://huggingface.co/qihoo360/RevealLayer models/RevealLayer
-```
-
-Download FLUX.1-dev:
-
-```bash
-git clone https://huggingface.co/black-forest-labs/FLUX.1-dev models/FLUX.1-dev
-```
-
-The expected model directory structure is:
-
-```text
-models
-├── RevealLayer
-│   ├── pytorch_lora_weights.safetensors
-│   ├── layer_pe.pt
-│   ├── Refiner.pt
-│   ├── xvae
-│   │   └── transparent_decoder_ckpt.pth
-│   └── ...
-├── FLUX.1-dev
-│   ├── transformer
-│   ├── vae
-│   ├── text_encoder
-│   ├── text_encoder_2
-│   ├── tokenizer
-│   ├── tokenizer_2
-│   └── ...
-```
-
-If your local model directory is different, please modify the corresponding paths in the inference script.
-
----
-
-## 🗂️ Prepare input JSON
-
-The input JSON should contain a list of samples. Each sample should include the input image path and detected bounding boxes.
-
-Example:
-
-```json
-[
-  {
-    "imgid": "examples",
-    "full_image": "RevealLayer-Bench/examples/full_image.png",
-    "background": "RevealLayer-Bench/examples/background.png",
-    "LayerInfoRaw": [
-      "RevealLayer-Bench/examples/layer_0.png",
-      "RevealLayer-Bench/examples/layer_1.png"
-    ],
-    "detections": [
-      {
-        "bbox": [x1, y1, x2, y2]
-      },
-      {
-        "bbox": [x1, y1, x2, y2]
-      }
-    ]
-  }
-]
-```
-
-The expected fields are:
-
-```text
-imgid        : sample id
-full_image   : path to the input RGB image
-background   : path to the background image, optional for inference
-LayerInfoRaw : paths to the ground-truth RGBA layers, optional for inference
-detections   : detected foreground objects
-bbox         : bounding box in [x1, y1, x2, y2] format
-```
-
----
-
-## ⚡ Inference
-
-Run inference with:
-
-```bash
-bash infer.sh 0
-```
-
-Before running, please make sure the paths in `infer.sh` and `infer_new.py` match your local model and data directories.
 
 ---