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readme.md

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-# DreamGaussian
-
-This repository contains the official implementation for [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://arxiv.org/abs/2309.16653).
-
-### [Project Page](https://dreamgaussian.github.io) | [Arxiv](https://arxiv.org/abs/2309.16653)
-
-
-https://github.com/dreamgaussian/dreamgaussian/assets/25863658/db860801-7b9c-4b30-9eb9-87330175f5c8
-
-### [Colab demo](https://github.com/camenduru/dreamgaussian-colab)
-* Image-to-3D: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sLpYmmLS209-e5eHgcuqdryFRRO6ZhFS?usp=sharing)
-* Text-to-3D: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/dreamgaussian-colab/blob/main/dreamgaussian_colab.ipynb)
-
-### [Gradio demo](https://huggingface.co/spaces/jiawei011/dreamgaussian)
-* Image-to-3D: <a href="https://huggingface.co/spaces/jiawei011/dreamgaussian"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Gradio%20Demo-Huggingface-orange"></a>
-
-## Install
-```bash
-pip install -r requirements.txt
-
-# a modified gaussian splatting (+ depth, alpha rendering)
-git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
-pip install ./diff-gaussian-rasterization
-
-# simple-knn
-pip install ./simple-knn
-
-# nvdiffrast
-pip install git+https://github.com/NVlabs/nvdiffrast/
-
-# kiuikit
-pip install git+https://github.com/ashawkey/kiuikit
-```
-
-Tested on:
-* Ubuntu 22 with torch 1.12 & CUDA 11.6 on a V100.
-* Windows 10 with torch 2.1 & CUDA 12.1 on a 3070.
-
-## Usage
-
-Image-to-3D:
-```bash
-### preprocess
-# background removal and recentering, save rgba at 256x256
-python process.py data/name.jpg
-
-# save at a larger resolution
-python process.py data/name.jpg --size 512
-
-# process all jpg images under a dir
-python process.py data
-
-### training gaussian stage
-# train 500 iters (~1min) and export ckpt & coarse_mesh to logs
-python main.py --config configs/image.yaml input=data/name_rgba.png save_path=name
-
-# gui mode (supports visualizing training)
-python main.py --config configs/image.yaml input=data/name_rgba.png save_path=name gui=True
-
-# load and visualize a saved ckpt
-python main.py --config configs/image.yaml load=logs/name_model.ply gui=True
-
-# use an estimated elevation angle if image is not front-view (e.g., common looking-down image can use -30)
-python main.py --config configs/image.yaml input=data/name_rgba.png save_path=name elevation=-30
-
-### training mesh stage
-# auto load coarse_mesh and refine 50 iters (~1min), export fine_mesh to logs
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name
-
-# specify coarse mesh path explicity
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name mesh=logs/name_mesh.obj
-
-# gui mode
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name gui=True
-
-# export glb instead of obj
-python main2.py --config configs/image.yaml input=data/name_rgba.png save_path=name mesh_format=glb
-
-### visualization
-# gui for visualizing mesh
-python -m kiui.render logs/name.obj
-
-# save 360 degree video of mesh (can run without gui)
-python -m kiui.render logs/name.obj --save_video name.mp4 --wogui
-
-# save 8 view images of mesh (can run without gui)
-python -m kiui.render logs/name.obj --save images/name/ --wogui
-
-### evaluation of CLIP-similarity
-python -m kiui.cli.clip_sim data/name_rgba.png logs/name.obj
-```
-Please check `./configs/image.yaml` for more options.
-
-Text-to-3D:
-```bash
-### training gaussian stage
-python main.py --config configs/text.yaml prompt="a photo of an icecream" save_path=icecream
-
-### training mesh stage
-python main2.py --config configs/text.yaml prompt="a photo of an icecream" save_path=icecream
-```
-Please check `./configs/text.yaml` for more options.
-
-Helper scripts:
-```bash
-# run all image samples (*_rgba.png) in ./data
-python scripts/runall.py --dir ./data --gpu 0
-
-# run all text samples (hardcoded in runall_sd.py)
-python scripts/runall_sd.py --gpu 0
-
-# export all ./logs/*.obj to mp4 in ./videos
-python scripts/convert_obj_to_video.py --dir ./logs
-```
-
-### Gradio Demo
-```bash
-python gradio_app.py
-```
-
-## Acknowledgement
-
-This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
-
-* [gaussian-splatting](https://github.com/graphdeco-inria/gaussian-splatting) and [diff-gaussian-rasterization](https://github.com/graphdeco-inria/diff-gaussian-rasterization)
-* [threestudio](https://github.com/threestudio-project/threestudio)
-* [nvdiffrast](https://github.com/NVlabs/nvdiffrast)
-* [dearpygui](https://github.com/hoffstadt/DearPyGui)
-
-## Citation
-
-```
-@article{tang2023dreamgaussian,
-  title={DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation},
-  author={Tang, Jiaxiang and Ren, Jiawei and Zhou, Hang and Liu, Ziwei and Zeng, Gang},
-  journal={arXiv preprint arXiv:2309.16653},
-  year={2023}
-}
-```
+---
+title: DreamGaussian
+emoji: 🐠
+colorFrom: purple
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.22.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference  
+Paper is from https://arxiv.org/abs/2309.16653