Update README.md

README.md

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-# Wonder3D
-Single Image to 3D using Cross-Domain Diffusion
-## [Paper](https://arxiv.org/abs/2310.15008) | [Project page](https://www.xxlong.site/Wonder3D/) 
-
-![](assets/fig_teaser.png)
-
-Wonder3D reconstructs highly-detailed textured meshes from a single-view image in only 2 ∼ 3 minutes. Wonder3D first generates consistent multi-view normal maps with corresponding color images via a cross-domain diffusion model, and then leverages a novel normal fusion method to achieve fast and high-quality reconstruction.
-
-## Schedule
-- [x] Inference code and pretrained models.
-- [ ] Huggingface demo.
-- [ ] Training code.
-- [ ] Rendering code for data prepare.
-
-
-### Preparation for inference
-1. Install packages in `requirements.txt`. 
-```angular2html
-conda create -n wonder3d
-conda activate wonder3d
-pip install -r requirements.txt
-```
-2. Download the [checkpoints](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/xxlong_connect_hku_hk/EgSHPyJAtaJFpV_BjXM3zXwB-UMIrT4v-sQwGgw-coPtIA) into the root folder.
-
-### Inference
-1. Make sure you have the following models.
-```bash
-Wonder3D
-|-- ckpts
-    |-- unet
-    |-- scheduler.bin
-    ...
-```
-2. Predict foreground mask as the alpha channel. We use [Clipdrop](https://clipdrop.co/remove-background) to segment the foreground object interactively. 
-You may also use `rembg` to remove the backgrounds.
-```bash
-# !pip install rembg
-import rembg
-result = rembg.remove(result)
-result.show()
-```
-3. Run Wonder3d to produce multiview-consistent normal maps and color images. Then you can check the results in the folder `./outputs`. (we use rembg to remove backgrounds of the results, but the segmemtations are not always perfect.) 
-```bash
-accelerate launch --config_file 1gpu.yaml test_mvdiffusion_seq.py \
-            --config mvdiffusion-joint-ortho-6views.yaml
-```
-or 
-```bash
-bash run_test.sh
-```
-
-4. Mesh Extraction
-```bash
-cd ./instant-nsr-pl
-bash run.sh output_folder_path scene_name
-```
-
-## Citation
-If you find this repository useful in your project, please cite the following work. :)
-```
-@misc{long2023wonder3d,
-      title={Wonder3D: Single Image to 3D using Cross-Domain Diffusion}, 
-      author={Xiaoxiao Long and Yuan-Chen Guo and Cheng Lin and Yuan Liu and Zhiyang Dou and Lingjie Liu and Yuexin Ma and Song-Hai Zhang and Marc Habermann and Christian Theobalt and Wenping Wang},
-      year={2023},
-      eprint={2310.15008},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+---
+title: Wonder3D
+emoji: 🚀
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 3.43.2
+app_file: app.py
+pinned: false
+license: cc-by-sa-3.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference