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NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing

Project Page | Video | Paper

NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing

[Bangbang Yang, Chong Bao]Co-Authors, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, Guofeng Zhang.

ECCV 2022 Oral

Installation

We have tested the code on Python 3.8.0 and PyTorch 1.8.1, while a newer version of pytorch should also work. The steps of installation are as follows:

  • create virtual environmental: conda env create --file environment.yml
  • install pytorch 1.8.1: pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
  • install open3d development version: pip install [open3d development package url]
  • install FRNN, a fixed radius nearest neighbors search implemented on CUDA.

Data

We use DTU data of NeuS version and NeRF synthetic data.

[Update]: We release the test image names for our pre-trained model in the DTU dataset, which is randomly selected for evaluating PSNR/SSIM/LPIPS. Each sequence has a val_names.txt that contains the names of test images.

P.S. Please enable the intrinsic_from_cammat: True for hotdog, chair, mic if you use the provided NeRF synthetic dataset.

Train

Here we show how to run our code on one example scene. Note that the data_dir should be specified in the configs/*.yaml.

  1. Train the teacher network (NeuS) from multi-view images.
python train.py --config configs/neus_dtu_scan63.yaml
  1. Extract a triangle mesh from a trained teacher network.
python extract_mesh.py --config configs/neus_dtu_scan63.yaml --ckpt_path logs/neus_dtuscan63/ckpts/latest.pt --output_dir out/neus_dtuscan63/mesh
  1. Train NeuMesh from multi-view images and the teacher network. Note that the prior_mesh, teacher_ckpt, teacher_config should be specified in the neumesh*.yaml
python train.py --config configs/neumesh_dtu_scan63.yaml

Evaluation

Here we provide all pre-trained models of DTU and NeRF synthetic dataset.

You can evaluate images with the trained models.

python -m render --config configs/neumesh_dtu_scan63.yaml   --load_pt logs/neumesh_dtuscan63/ckpts/latest.pt --camera_path spiral --background 1 --test_frame 24 --spiral_rad 1.2

P.S. If the time of inference costs too much, --downscale can be enabled for acceleration.

Manipulation

Please refer to editing/README.md.

Citing

@inproceedings{neumesh,
    title={NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing},
    author={{Chong Bao and Bangbang Yang} and Zeng Junyi and Bao Hujun and Zhang Yinda and Cui Zhaopeng and Zhang Guofeng},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2022}
}

Note: joint first-authorship is not really supported in BibTex; you may need to modify the above if not using CVPR's format. For the SIGGRAPH (or ACM) format you can try the following:

@inproceedings{neumesh,
    title={NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing},
    author={{Bao and Yang} and Zeng Junyi and Bao Hujun and Zhang Yinda and Cui Zhaopeng and Zhang Guofeng},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2022}
}

Acknowledgement

In this project we use parts of the implementations of the following works:

We thank the respective authors for open sourcing their methods.