Improve model card with pipeline tag, links, and usage example

README.md

@@ -1,3 +1,72 @@
 ---
 license: mit
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+pipeline_tag: image-to-3d
+---
+
+# Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy
+
+This repository contains the Gen-3Diffusion model presented in the paper [Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy](https://huggingface.co/papers/2412.06698).
+
+Gen-3Diffusion addresses the challenging problem of creating realistic 3D objects and clothed avatars from a single RGB image. It leverages a pre-trained 2D diffusion model and a 3D diffusion model, synchronizing them at both training and sampling time. This synergy allows the 2D model to provide strong generalization for shapes, while the 3D model enhances multi-view consistency, leading to high-fidelity geometry and texture in generated 3D objects and avatars.
+
+- [Project Page](https://yuxuan-xue.com/gen-3diffusion)
+- [Code](https://github.com/YuxuanSnow/Gen3Diffusion)
+
+## 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**
+
+## Pretrained Weights
+Our pretrained weights can be downloaded from Hugging Face.
+```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 ..
+```
+
+## Sample Usage (Inference)
+The following commands illustrate how to use the model for image-to-3D object and avatar generation. Please refer to the [GitHub repository](https://github.com/YuxuanSnow/Gen3Diffusion) for full installation and setup instructions.
+
+```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:
+If you find our work helpful or inspiring, please feel free to cite it:
+```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},
+}
+```