Enhance model card with metadata, links, and usage examples

This PR significantly enhances the model card by adding:
- The `pipeline_tag: image-to-3d` to improve discoverability on the Hub.
- Direct links to the paper, project page, and GitHub repository.
- Comprehensive usage examples for installation, pretrained weights, and inference, extracted directly from the GitHub README.

Please review and merge if everything looks good!

README.md

@@ -1,3 +1,94 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-to-3d
+---
+
+# Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy
+
+This repository contains the `Gen-3Diffusion` model, which achieves realistic image-to-3D generation by leveraging a pre-trained 2D diffusion model and a 3D diffusion model, as presented in the paper:
+[**Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy**](https://huggingface.co/papers/2412.06698)
+
+Project Page: [https://yuxuan-xue.com/gen-3diffusion](https://yuxuan-xue.com/gen-3diffusion)
+Code: [https://github.com/YuxuanSnow/Gen3Diffusion](https://github.com/YuxuanSnow/Gen3Diffusion)
+
+![](https://github.com/YuxuanSnow/Gen3Diffusion/blob/main/assets/teaser_video.gif)
+
+## Key Insight :raised_hands:
+- 2D foundation models are powerful but output lacks 3D consistency!
+- 3D generative models can reconstruct 3D representation but is poor in generalization!
+- How to combine 2D foundation models with 3D generative models?:
+  - they are both diffusion-based generative models => **Can be synchronized at each diffusion step**
+  - 2D foundation model helps 3D generation => **provides strong prior informations about 3D shape**
+  - 3D representation guides 2D diffusion sampling => **use rendered output from 3D reconstruction for reverse sampling, where 3D consistency is guaranteed**
+
+## Install
+Same Conda environment to Human-3Diffusion. Please skip if you already installed it.
+```bash
+# Conda environment
+conda create -n gen3diffusion python=3.10
+conda activate gen3diffusion
+pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
+pip install xformers==0.0.22.post4 --index-url https://download.pytorch.org/whl/cu121
+
+# Gaussian Opacity Fields
+git clone https://github.com/YuxuanSnow/gaussian-opacity-fields.git
+cd gaussian-opacity-fields && pip install submodules/diff-gaussian-rasterization
+pip install submodules/simple-knn/ && cd ..
+export CPATH=/usr/local/cuda-12.1/targets/x86_64-linux/include:$CPATH
+
+# Dependencies
+pip install -r requirements.txt
+
+# TSDF Fusion (Mesh extraction) Dependencies
+pip install --user numpy opencv-python scikit-image numba
+pip install --user pycuda
+pip install scipy==1.11
+```
+
+## Pretrained Weights
+Our pretrained weight can be downloaded from huggingface.
+```bash
+mkdir checkpoints_obj && cd checkpoints_obj
+wget https://huggingface.co/yuxuanx/gen3diffusion/resolve/main/model.safetensors
+wget https://huggingface.co/yuxuanx/gen3diffusion/resolve/main/model_1.safetensors
+wget https://huggingface.co/yuxuanx/gen3diffusion/resolve/main/pifuhd.pt
+cd ..
+```
+The avatar reconstruction module is same to Human-3Diffusion. Please skip if you already installed Human-3Diffusion.
+```bash
+mkdir checkpoints_avatar && cd checkpoints_avatar
+wget https://huggingface.co/yuxuanx/human3diffusion/resolve/main/model.safetensors
+wget https://huggingface.co/yuxuanx/human3diffusion/resolve/main/model_1.safetensors
+wget https://huggingface.co/yuxuanx/human3diffusion/resolve/main/pifuhd.pt
+cd ..
+```
+
+## Inference
+```bash
+# given one image of object, generate 3D-GS object
+# subject should be centered in a square image, please crop properly 
+# recenter plays a huge role in object reconstruction. Please adjust the recentering if the reconstruction doesn't work well
+python infer.py --test_imgs test_imgs_obj --output output_obj --checkpoints checkpoints_obj
+
+# given generated 3D-GS, perform TSDF mesh extraction
+python infer_mesh.py --test_imgs test_imgs_obj --output output_obj --checkpoints checkpoints_obj --mesh_quality high
+```
+
+```bash 
+# given one image of human, generate 3D-GS avatar
+# subject should be centered in a square image, please crop properly
+python infer.py --test_imgs test_imgs_avatar --output output_avatar --checkpoints checkpoints_avatar
+
+# given generated 3D-GS, perform TSDF mesh extraction
+python infer_mesh.py --test_imgs test_imgs_avatar --output output_avatar --checkpoints checkpoints_avatar --mesh_quality high
+```
+
+## Citation :writing_hand:
+```bibtex
+@inproceedings{xue2024gen3diffusion,
+  title     = {{Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy }},
+  author    = {Xue, Yuxuan and Xie, Xianghui and Marin, Riccardo and Pons-Moll, Gerard.},\
+  journal   = {Arxiv},\
+  year      = {2024},\
+}
+```