Improve model card with comprehensive details and metadata

This PR significantly enhances the model card for LangScene-X, making it much more informative and discoverable on the Hugging Face Hub.

Key improvements include:
- Populating the `README.md` with detailed information from the project's GitHub repository, including an introduction, news, abstract, pipeline overview, video demos, installation instructions, and usage examples.
- Adding the `pipeline_tag: image-to-3d` to the metadata, ensuring the model appears in relevant searches on the Hub.
- Specifying `library_name: diffusers` to enable proper integration and display of the model with the Diffusers library.
- Consolidating all relevant links (paper, project page, code, arXiv) for easy access.

Please review and merge this PR to make this exciting research more accessible to the community!

README.md

@@ -1,8 +1,137 @@
 ---
 license: mit
+pipeline_tag: image-to-3d
+library_name: diffusers
 ---
-# LangScene-X
 
-- Repository: https://github.com/liuff19/LangScene-X/
-- Project Page: https://liuff19.github.io/LangScene-X/
-- arXiv: https://arxiv.org/abs/2507.02813
+<div align="center">
+
+# ✨LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion✨
+
+<p align="center">
+<a href="https://liuff19.github.io/">Fangfu Liu</a><sup>1</sup>,
+<a href="https://lifuguan.github.io/">Hao Li</a><sup>2</sup>,
+<a href="https://github.com/chijw">Jiawei Chi</a><sup>1</sup>,
+<a href="https://hanyang-21.github.io/">Hanyang Wang</a><sup>1,3</sup>,
+<a href="https://github.com/liuff19/LangScene-X">Minghui Yang</a><sup>3</sup>,
+<a href="https://github.com/liuff19/LangScene-X">Fudong Wang</a><sup>3</sup>,   
+<a href="https://duanyueqi.github.io/">Yueqi Duan</a><sup>1</sup>
+<br>
+    <sup>1</sup>Tsinghua University, <sup>2</sup>NTU, <sup>3</sup>Ant Group     
+</p>
+<h3 align="center">ICCV 2025 🔥</h3>
+<a href="https://arxiv.org/abs/2507.02813"><img src='https://img.shields.io/badge/arXiv-2507.02813-b31b1b.svg'></a> &nbsp;&nbsp;&nbsp;&nbsp;
+<a href="https://liuff19.github.io/LangScene-X"><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;&nbsp;&nbsp;&nbsp;
+<a href="https://huggingface.co/chijw/LangScene-X"><img src='https://img.shields.io/badge/LangSceneX-huggingface-yellow'></a> &nbsp;&nbsp;&nbsp;&nbsp;
+<a><img src='https://img.shields.io/badge/License-MIT-blue'></a> &nbsp;&nbsp;&nbsp;&nbsp;
+
+![Teaser Visualization](https://github.com/liuff19/LangScene-X/blob/main/assets/teaser.png?raw=true)
+</div>
+
+**LangScene-X:** We propose LangScene-X, a unified model that generates RGB, segmentation map, and normal map, enabling to reconstruct 3D field from sparse views input.
+
+## 📄 Paper
+The model was presented in the paper [LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion](https://huggingface.co/papers/2507.02813).
+
+## 🔗 Links
+- Repository: [https://github.com/liuff19/LangScene-X/](https://github.com/liuff19/LangScene-X/)
+- Project Page: [https://liuff19.github.io/LangScene-X/](https://liuff19.github.io/LangScene-X/)
+- arXiv: [https://arxiv.org/abs/2507.02813](https://arxiv.org/abs/2507.02813)
+
+## 📖 Abstract
+
+Recovering 3D structures with open-vocabulary scene understanding from 2D images is a fundamental but daunting task. Recent developments have achieved this by performing per-scene optimization with embedded language information. However, they heavily rely on the calibrated dense-view reconstruction paradigm, thereby suffering from severe rendering artifacts and implausible semantic synthesis when limited views are available. In this paper, we introduce a novel generative framework, coined LangScene-X, to unify and generate 3D consistent multi-modality information for reconstruction and understanding. Powered by the generative capability of creating more consistent novel observations, we can build generalizable 3D language-embedded scenes from only sparse views. Specifically, we first train a TriMap video diffusion model that can generate appearance (RGBs), geometry (normals), and semantics (segmentation maps) from sparse inputs through progressive knowledge integration. Furthermore, we propose a Language Quantized Compressor (LQC), trained on large-scale image datasets, to efficiently encode language embeddings, enabling cross-scene generalization without per-scene retraining. Finally, we reconstruct the language surface fields by aligning language information onto the surface of 3D scenes, enabling open-ended language queries. Extensive experiments on real-world data demonstrate the superiority of our LangScene-X over state-of-the-art methods in terms of quality and generalizability.
+
+## 📢 News
+- 🔥 [04/07/2025] We release "LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion". Check our [project page](https://liuff19.github.io/LangScene-X) and [arXiv paper](https://arxiv.org/abs/2507.02813).
+
+## 🌟 Pipeline
+
+![Pipeline Visualization](https://github.com/liuff19/LangScene-X/blob/main/assets/pipeline.png?raw=true)
+
+Pipeline of LangScene-X. Our model is composed of a TriMap Video Diffusion model which generates RGB, segmentation map, and normal map videos, an Auto Encoder that compresses the language feature, and a field constructor that reconstructs 3DGS from the generated videos. 
+
+
+## 🎨 Video Demos from TriMap Video Diffusion
+
+https://github.com/user-attachments/assets/55346d53-eb04-490e-bb70-64555e97e040
+
+https://github.com/user-attachments/assets/d6eb28b9-2af8-49a7-bb8b-0d4cba7843a5
+
+https://github.com/user-attachments/assets/396f11ef-85dc-41de-882e-e249c25b9961
+
+## ⚙️ Setup
+
+### 1. Clone Repository
+```bash
+git clone https://github.com/liuff19/LangScene-X.git
+cd LangScene-X
+```
+### 2. Environment Setup
+
+1. **Create conda environment**
+
+```bash
+conda create -n langscenex python=3.10 -y
+conda activate langscenex
+```
+2. **Install dependencies**
+```bash
+conda install pytorch torchvision -c pytorch -y
+pip install -e field_construction/submodules/simple-knn
+pip install -e field_construction/submodules/diff-langsurf-rasterizer
+pip install -e auto-seg/submodules/segment-anything-1
+pip install -e auto-seg/submodules/segment-anything-2
+pip install -r requirements.txt
+```
+
+### 3. Model Checkpoints
+The checkpoints of SAM, SAM2 and fine-tuned CogVideoX can be downloaded from our [huggingface repository](https://huggingface.co/chijw/LangScene-X).
+
+## 💻Running
+
+### Quick Start
+You can start quickly by running the following scripts:
+```bash
+chmod +x quick_start.sh
+./quick_start.sh <first_rgb_image_path> <last_rgb_image_path>
+```
+### Render
+Run the following command to render from the reconstructed 3DGS field:
+```bash 
+python entry_point.py \
+    pipeline.rgb_video_path="does/not/matter" \
+    pipeline.normal_video_path="does/not/matter" \
+    pipeline.seg_video_path="does/not/matter" \
+    pipeline.data_path="does/not/matter" \
+    gaussian.dataset.source_path="does/not/matter" \
+    gaussian.dataset.model_path="output/path" \
+    pipeline.selection=False \
+    gaussian.opt.max_geo_iter=1500 \
+    gaussian.opt.normal_optim=True \
+    gaussian.opt.optim_pose=True \
+    pipeline.skip_video_process=True \
+    pipeline.skip_lang_feature_extraction=True \
+    pipeline.mode="render"
+```
+You can also configurate by editting `configs/field_construction.yaml`.
+
+## 🔗Acknowledgement
+
+We are thankful for the following great works when implementing LangScene-X:
+
+- [CogVideoX](https://github.com/THUDM/CogVideo), [CogvideX-Interpolation](https://github.com/feizc/CogvideX-Interpolation), [LangSplat](https://github.com/minghanqin/LangSplat), [LangSurf](https://github.com/lifuguan/LangSurf), [VGGT](https://github.com/facebookresearch/vggt), [3DGS](https://github.com/graphdeco-inria/gaussian-splatting), [SAM2](https://github.com/facebookresearch/sam2)
+
+## 📚Citation
+
+```bibtex
+@misc{liu2025langscenexreconstructgeneralizable3d,
+      title={LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion}, 
+      author={Fangfu Liu and Hao Li and Jiawei Chi and Hanyang Wang and Minghui Yang and Fudong Wang and Yueqi Duan},
+      year={2025},
+      eprint={2507.02813},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.02813}, 
+}
+```