Update README.md

README.md

@@ -1,4 +1,4 @@
----
+```yaml
 license: cc-by-nc-sa-4.0
 language:
 - en
@@ -10,65 +10,64 @@ tags:
 - siggraph-asia-2026
 - wan2.1
 pipeline_tag: image-to-image
----
+```
 
-<p align="center">
-  <svg width="100%" height="100" viewBox="0 0 1000 100" xmlns="http://w3.org">
-    <defs>
-      <!-- 垂直多点渐变：模拟金属表面对光源（Relighting）的反射和暗面折射 -->
-      <linearGradient id="relightingMetal" x1="0%" y1="0%" x2="0%" y2="100%">
-        <!-- 顶部：金属边缘高光，在深色背景下勾勒轮廓 -->
-        <stop offset="0%" style="stop-color:#ffffff; stop-opacity:1" />
-        <!-- 上中部：光照直射区，高亮铝合金质感（浅色模式下依然清晰） -->
-        <stop offset="25%" style="stop-color:#d1d5db; stop-opacity:1" />
-        <!-- 中下部：金属核心暗面，呈现深钛合金色，提供深色对比度 -->
-        <stop offset="70%" style="stop-color:#1f2937; stop-opacity:1" />
-        <!-- 底部：地面或环境光的反光（Glow/Reflection Effect） -->
-        <stop offset="100%" style="stop-color:#9ca3af; stop-opacity:1" />
-      </linearGradient>
-    </defs>
-    <!-- 使用加粗现代科技字体，字间距拉开至 5，突出三维立体的分量感 -->
-    <text x="50%" y="55%" font-family="'Montserrat', 'Helvetica Neue', 'Segoe UI', sans-serif" font-size="75" font-weight="900" fill="url(#relightingMetal)" text-anchor="middle" dominant-baseline="middle" letter-spacing="5">RelightFormer</text>
-  </svg>
-</p>
+<div align="center">
 
+  <h1>✨ RelightFormer ✨</h1>
 
+  <h2>Feed-Forward Multi-View Relighting  
+with Generative Transformers</h2>
+
+</div>
 
 <p align="center">
-  <img src="https://raw.githubusercontent.com/vLAR-group/RelightFormer/main/demo/teaser.jpg" alt="Teaser" width="80%">
+  <img
+    src="https://raw.githubusercontent.com/vLAR-group/RelightFormer/main/demo/teaser.jpg"
+    alt="RelightFormer teaser showing photorealistic multi-view relighting results"
+    width="80%"
+  >
 </p>
 
-
 <p align="center">
   <strong>vLAR Group</strong> | <em>SIGGRAPH Asia 2026</em>
 </p>
 
 <p align="center">
-  <a href="https://github.com/vLAR-group/RelightFormer">
-    <img src="https://img.shields.io/badge/Code-GitHub-black" alt="Code">
+  <a href="https://arxiv.org/abs/2609.07414">
+    <img src="https://img.shields.io/badge/arXiv-2609.07414-b31b1b.svg" alt="arXiv">
   </a>
   <a href="https://huggingface.co/datasets/vLAR/LavalObjaverseDataset">
-    <img src="https://img.shields.io/badge/Dataset-Hugging%20Face-yellow" alt="Dataset">
+    <img src="https://img.shields.io/badge/🤗-Dataset-yellow" alt="Dataset">
+  </a>
+  <a href="https://huggingface.co/vLAR/RelightFormer">
+    <img src="https://img.shields.io/badge/🤗-Model-yellow" alt="Model">
+  </a>
+  <a href="#license">
+    <img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg" alt="License">
   </a>
 </p>
 
 
-
 ## 🌟 Overview
 
-**RelightFormer** revolutionizes image relighting by replacing traditional, computationally expensive inverse rendering with a feed-forward generative Transformer. By seamlessly injecting target lighting into spatial features and processing multiple views symmetrically, it delivers highly photorealistic results. Trained on the newly introduced, large-scale open-source **Laval-Objaverse Dataset (LOD)**, RelightFormer achieves state-of-the-art quality and remarkable generalization across diverse scenes.
+**RelightFormer** revolutionizes image relighting by replacing traditional, computationally expensive inverse rendering with a feed-forward generative Transformer. By seamlessly injecting target lighting into spatial features and processing multiple views symmetrically, it delivers highly photorealistic results. Trained on the newly introduced, large-scale open-source **Laval-Objaverse Dataset (LOD )**, RelightFormer achieves state-of-the-art quality and remarkable generalization across diverse scenes.
 
 ### ✨ Key Features
+
 - 🏹 **Feed-Forward Architecture**: No iterative optimization required, enabling rapid generation.
+
 - 🌟 **Multi-View Consistency**: Coherent and physically plausible relighting across all viewpoints.
+
 - ⚡ **Performant Inference**: Highly optimized and expeditious execution on modern GPUs.
+
 - 🎨 **Competitive Quality**: State-of-the-art, photorealistic relighting results.
 
 ---
 
 ## 🚀 Quick Start
 
-You can easily load and run the model using the `diffsynth` library in our [GitHub Repository](https://github.com/vLAR-group/RelightFormer). 
+You can easily load and run the model using the `diffsynth` library in our [GitHub Repository](https://github.com/vLAR-group/RelightFormer).
 We provide two revisions: `main` (RelightFormer) and `post` (RelightFormer-Post, fine-tuned for enhanced quality).
 
 ```python
@@ -84,14 +83,16 @@ pipe = RelightFormerPipeline.from_pretrained(
 # output = pipe(image=..., lighting=..., ...)
 ```
 
-> 💡 **For full inference scripts, multi-GPU evaluation, and training code, please visit the [Official GitHub Repository](https://github.com/vLAR-group/RelightFormer).**
+> 💡 **For full inference scripts, multi-GPU evaluation, and training code, please visit the **[**Official GitHub Repository**](https://github.com/vLAR-group/RelightFormer)**.**
 
 ---
 
 ## 📦 Dataset
 
-This model is trained on the **Laval-Objaverse Dataset (LOD)**, comprising **90,545 high-quality 3D assets** and **39,008 diverse illumination conditions**. 
+This model is trained on the **Laval-Objaverse Dataset (LOD)**, comprising **90,545 high-quality 3D assets** and **39,008 diverse illumination conditions**.
+
 - 🤗 **Browse/Download the Dataset**: [vLAR/LavalObjaverseDataset](https://huggingface.co/datasets/vLAR/LavalObjaverseDataset)
+
 - 📖 **Rendering Instructions**: See the [`RENDERING_INSTRUCTION.md`](https://github.com/vLAR-group/RelightFormer/blob/main/laval-objaverse-dataset/RENDERING_INSTRUCTION.md) in the GitHub repo.
 
 ---
@@ -99,8 +100,10 @@ This model is trained on the **Laval-Objaverse Dataset (LOD)**, comprising **90,
 ## 🏋️ Training Details
 
 RelightFormer is fine-tuned from the **Wan 2.1** base model. The training pipeline consists of two stages:
+
 1. **Main Training**: Trained on the full LOD dataset to learn multi-view relighting priors.
-2. **Post-Training**: A secondary fine-tuning stage to further enhance photorealism and consistency.
+
+1. **Post-Training**: A secondary fine-tuning stage to further enhance photorealism and consistency.
 
 Detailed training configurations, hardware requirements (e.g., 4× H200 GPUs), and scripts are available in the [GitHub Repository](https://github.com/vLAR-group/RelightFormer).
 
@@ -108,10 +111,10 @@ Detailed training configurations, hardware requirements (e.g., 4× H200 GPUs), a
 
 ## 📜 License
 
-This model, its code, and associated datasets are licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC BY-NC-SA 4.0). 
+This model, its code, and associated datasets are licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC BY-NC-SA 4.0).
 
 ---
 
 ## 🙏 Acknowledgements
 
-This work was supported in part by the National Natural Science Foundation of China, the Research Grants Council of Hong Kong, the Otto Poon Charitable Foundation Smart Cities Research Institute, the Research Center for Unmanned Autonomous Systems, and the PolyU Kunpeng & Ascend Technology Innovation Incubation Center, The Hong Kong Polytechnic University.
+This work was supported in part by the National Natural Science Foundation of China, the Research Grants Council of Hong Kong, the Otto Poon Charitable Foundation Smart Cities Research Institute, the Research Center for Unmanned Autonomous Systems, and the PolyU Kunpeng & Ascend Technology Innovation Incubation Center, The Hong Kong Polytechnic University.