Improve model card: Add pipeline tag, update license, and enrich content

README.md

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-license: apache-2.0
+license: cc-by-nc-4.0
+pipeline_tag: image-to-3d
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-Here are the checkpoints of DiscretizedSDF in ICCV 2025. 
-Find more information of our paper from [Github](https://github.com/NK-CS-ZZL/DiscretizedSDF) or [Project Page](https://nk-cs-zzl.github.io/projects/dsdf/index.html).
+
+# Gaussian Splatting with Discretized SDF for Relightable Assets
+
+This repository contains the official checkpoints for **DiscretizedSDF**, a novel method presented in the paper [Gaussian Splatting with Discretized SDF for Relightable Assets](https://huggingface.co/papers/2507.15629). This work will be presented at ICCV 2025.
+
+DiscretizedSDF significantly advances 3D Gaussian splatting (3DGS) for inverse rendering, enabling the creation of high-quality relightable 3D assets. It addresses common challenges in applying geometry constraints to discrete Gaussian primitives and the high memory costs associated with continuous Signed Distance Fields (SDFs).
+
+Key innovations include:
+-   **Discretized SDF**: A novel approach to represent continuous SDFs discretely, encoding sampled values within each Gaussian. This allows for efficient SDF rendering via splatting, bypassing costly ray tracing.
+-   **Projection-based Consistency Loss**: A unique regularization technique that projects Gaussians onto the SDF's zero-level set, ensuring geometric alignment with the splatted surface.
+
+Thanks to these innovations, DiscretizedSDF achieves superior relighting quality without additional memory overhead compared to standard 3DGS, and it simplifies the training process by avoiding complex manual optimizations.
+
+<p align="middle">
+<img src="https://github.com/NK-CS-ZZL/DiscretizedSDF/raw/main/demo/horse_golf.gif" width="30%"/><img src="https://github.com/NK-CS-ZZL/DiscretizedSDF/raw/main/demo/angel_corridor.gif" width="30%"/><img src="https://github.com/NK-CS-ZZL/DiscretizedSDF/raw/main/demo/potion_golf.gif" width="30%"/>
+</p>
+
+## Links
+-   📄 **Paper**: [Gaussian Splatting with Discretized SDF for Relightable Assets](https://huggingface.co/papers/2507.15629)
+-   🌐 **Project Page**: [https://nk-cs-zzl.github.io/projects/dsdf/index.html](https://nk-cs-zzl.github.io/projects/dsdf/index.html)
+-   💻 **GitHub Repository**: [https://github.com/NK-CS-ZZL/DiscretizedSDF](https://github.com/NK-CS-ZZL/DiscretizedSDF)
+
+## Usage
+For detailed installation instructions, environment setup, and information on training and evaluation, please refer to the [official GitHub repository](https://github.com/NK-CS-ZZL/DiscretizedSDF).
+
+To run a quick relighting video demo with the provided checkpoints:
+
+1.  Clone the repository:
+    ```bash
+    git clone https://github.com/NK-CS-ZZL/DiscretizedSDF.git
+    cd DiscretizedSDF
+    ```
+2.  Follow the installation steps on the [GitHub repository's "Dependencies and Installation" section](https://github.com/NK-CS-ZZL/DiscretizedSDF#dependencies-and-installation) to set up the environment and dependencies.
+3.  Download pretrained models (e.g., from [HuggingFace](https://huggingface.co/lalala125/DiscreteSDF) as mentioned in the GitHub README) and place them in the `pretrained` folder.
+4.  Run the demo script:
+    ```bash
+    sh demo.sh
+    ```
+
+## Citation
+If you find our work useful for your research, please consider citing our paper:
+```bibtex
+@inproceedings{zhu_2025_dsdf,
+   title={Gaussian Splatting with Discretized SDF for Relightable Assets},
+   author={Zhu, Zuo-Liang and Yang, Jian and Wang, Beibei},
+   booktitle={Proceedings of IEEE International Conference on Computer Vision (ICCV)},
+   year={2025}
+}
+```