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Fancy123

(★ 68)

[CVPR2025]Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation

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- instantmesh.yaml
- cute_horse.jpg
- girl3.jpeg
- girl_1.jpeg
- girl_2.png
- SCHOOL_BUS.png
- - jacobian_deform.py
  - math_utils.py
  - poisson_system.py
  - PoissonSystem.py
- - camera_optimize.py
  - deform_2d.py
  - img_registration.py
  - mesh_deformation.py
  - mesh_optimize.py
- - general_renderer.py
- - NBF.py
- - logger_util.py
  - mesh_utils.py
  - normal_utils.py
  - pipelines.py
  - sample_colored_pc.py
  - temp_utils.py
  - utils2d.py
  - vtk_basic.py
- refine_one_sample.py
- steps.py
- instant-mesh-base.yaml
- instant-mesh-large-train.yaml
- instant-mesh-large.yaml
- instant-nerf-base.yaml
- instant-nerf-large-train.yaml
- instant-nerf-large.yaml
- zero123plus-finetune.yaml
- install.md
- - __init__.py
  - objaverse.py
  - objaverse_zero123plus.py
- - __init__.py
  - lrm.py
  - lrm_mesh.py
- - __init__.py
  - mesh_gen.py
  - mv_gen.py
  - prepare.py
  - save_video.py
- - __init__.py
  - camera_util.py
  - infer_util.py
  - mesh_util.py
  - render_util.py
  - train_util.py
- __init__.py
- model.py
- model_mesh.py
- - all_models.py
  - utils.py
- - func.py
  - opt.py
  - refine.py
  - remesh.py
  - render.py
- - all_typing.py
  - load_onnx copy.py
  - load_onnx.py
  - project_mesh.py
  - refine_lr_to_sr.py
  - sd_model_zoo.py
  - upsampler.py
  - utils.py
- model.py
- pipeline.py
.gitignore
main_fancy123_refine.py
README.md
requirements.txt
run_init.py

# Installation Guide

1. Get the code

git clone https://github.com/YuQiao0303/Fancy123

Downloads the entire project code from GitHub to your computer.

cd Fancy123

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# Fancy123
[![Paper](https://img.shields.io/badge/Paper-CVPR2025-006699.svg)](https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Fancy123_One_Image_to_High-Quality_3D_Mesh_Generation_via_Plug-and-Play_CVPR_2025_paper.html)    [![arXiv](https://img.shields.io/badge/arXiv-2411.16185-b31b1b.svg)](https://arxiv.org/abs/2411.16185)

This is the official repository for the CVPR2025 paper: *''Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation''* by Qiao Yu, Xianzhi Li, Yuan Tang, Xu Han, Long Hu, Yixue Hao, and Min Chen.

Fancy123 generates a high-quality textured 3D mesh given a single RGB image:

https://github.com/user-attachments/assets/470f1244-197a-472a-98d6-599775638ac7

Fancy123's core idea is to utilize 2D image deformation to address the multiview inconsistency issue, and 3D mesh deformation to address the low fidelity issue:

![method](https://github.com/user-attachments/assets/fdebcb8e-b98c-450c-8309-8139e547931c)

https://github.com/user-attachments/assets/6700ac0d-7331-4097-8cf3-3a3e7ede6b70

## 🎉 News
- 2025.07.21: Code Released! 🌟
- 2025.02.27: Fancy123 is accepted by **CVPR2025**!🎊 We currently plan to release code in July 2025.

## 🛠️Install & Model Download
Please refer to [scripts/install.md](scripts/install.md).

## ✅Training
Fancy123 works during inference and is training-free!

## 🎬Inference
First, put your images under `examples', then run InstantMesh to get an initial mesh:

```bash
python run_init.py
```
You'll see generated multiview images in outputs/instant-mesh-large/images and initial meshes in outputs/instant-mesh-large/meshes.

Then, run Fancy123's enhancement steps:
```bash
python main_fancy123_refine.py
```
You'll see results in outputs/instant-mesh-large/fancy123_meshes. 
You can use tools like MeshLab or Blender to visualize the final result named `final_mesh.obj'.

### Tips for better results
1. Check `unproject_wo_2D_deform.obj`: if its geometry seems bad, try  setting `geo_refine=False` in `main_fancy123_refine.py` L105.
2. if `2D_deform_deformed.obj` seems ok but `3D_deformed_mesh.obj`  looks smaller or shrunk,  you can try setting `lap_weight` smaller or even to 0  in `configs/instantmesh.yaml`. We found that a larger laplacian weight makes the mesh shrink.
3. if `2D_deform_deformed.obj` seems ok but `3D_deformed_mesh.obj` looks bad, it may be that no suitable camera parameters are found. If your input image is almost 0-elevation,  you can try setting `input_all_0_elevation` to True in `configs/instantmesh.yaml`. In this way we don't try to find the best camera parameters but use the default settings.
4. If something seems wrong with the texture projecting, try `use_vertex_wise_NBF: False` in `configs/instantmesh.yaml`.
If you still cannot get a good result, try other random seeds, or leave an issue so that I can see if there's anything that I can help.

## 🤝Acknowledgement
We have intensively borrowed code from the following repositories. Many thanks to the authors for sharing their code.

- [InstantMesh](https://github.com/TencentARC/InstantMesh)
- [Zero123++](https://github.com/SUDO-AI-3D/zero123plus)
- [Unique3D](https://github.com/AiuniAI/Unique3D)
- [APAP](https://github.com/KAIST-Visual-AI-Group/APAP)

## 📝Citation
If you find Fancy123 helpful, please cite our paper:
```
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Qiao and Li, Xianzhi and Tang, Yuan and Han, Xu and Hu, Long and Hao, Yixue and Chen, Min},
    title     = {Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {595-604}
}
```