MeshDiffusion: Score-based Generative 3D Mesh Modeling

Introduction

This is the official implementation of MeshDiffusion (ICLR 2023 Spotlight).

MeshDiffusion is a diffusion model for generating 3D meshes with a direct parametrization of deep marching tetrahedra (DMTet). Please refer to our project page for more details and interactive demos.

Getting Started

Requirements

• Python >= 3.8
• CUDA 11.6
• Pytorch >= 1.6
• Pytorch3D

Follow the instructions to install requirements for nvdiffrec

Pretrained Models

Download our pretrained MeshDiffusion models (resolution 64) for chair, car and airplane. As a backup option, you can also download the models for car and chair from Google Drive.

Download the res-128 models here: car and chair.

Inference

Unconditional Generation

Run the following

python main_diffusion.py --config=$DIFFUSION_CONFIG --mode=uncond_gen \
--config.eval.eval_dir=$OUTPUT_PATH \
--config.eval.ckpt_path=$CKPT_PATH

Later run

cd nvdiffrec
python eval.py --config $DMTET_CONFIG --out-dir $OUT_DIR --sample-path $SAMPLE_PATH --deform-scale $DEFORM_SCALE [--angle-ind $ANGLE_INDEX]

where $SAMPLE_PATH is the generated sample .npy file in $OUTPUT_PATH, and $DEFORM_SCALE is the scale of deformation of tet vertices set for the DMTet dataset (we use 3.0 for resolution 64 as default; change the value for your own datasets). Change $ANGLE_INDEX to some number from 0 to 50 if images rendered from different angles are desired.

A mesh file (.obj) will be saved to the folder, which can be viewed in tools such as MeshLab. The saved images are rendered from raw meshes without post-processing and thus are used for fast sanity check only.

Single-view Conditional Generation

First fit a DMTet from a single view of a mesh positioned in its canonical pose

cd nvdiffrec
python fit_singleview.py --config $DMTET_CONFIG --mesh-path $MESH_PATH --angle-ind $ANGLE_IND --out-dir $OUT_DIR --validate $VALIDATE

where $ANGLE_IND is an integer (0 to 50) controlling the z-axis rotation of the object. Set $VALIDATE to 1 if visualization of fitted DMTets is needed.

Then use the trained diffusion model to complete the occluded regions

cd ..

python main_diffusion.py --mode=cond_gen --config=$DIFFUSION_CONFIG \
--config.eval.eval_dir=$EVAL_DIR \
--config.eval.ckpt_path=$CKPT_PATH \
--config.eval.partial_dmtet_path=$OUT_DIR/tets/dmtet.pt \
--config.eval.tet_path=$TET_PATH \
--config.eval.batch_size=$EVAL_BATCH_SIZE

, in which $TET_PATH is the uniform tetrahedral grid (of resolution 64 or 128) file in nvdiffrec/data/tets.

Now store the completed meshes as .obj files in $SAMPLE_PATH

cd nvdiffrec
python eval.py --config $DMTET_CONFIG --sample-path $SAMPLE_PATH  --deform-scale $DEFORM_SCALE

Caution: the deformation scale should be consistent for single view fitting and the diffusion model. Check before you run conditional generation.

Training

For ShapeNet, first create a list of paths of all ground-truth meshes and store them as a json file under ./nvdiffrec/data/shapenet_json.

Then run the following

cd nvdiffrec
python fit_dmtets.py --config $DMTET_CONFIG --meta-path $META_PATH --out-dir $DMTET_DATA_PATH --index 0 --split-size 100000

where split_size is set to any large number greater than the dataset size. In case of batch fitting with multiple jobs, change split_size to a suitable number and assign a different index for different jobs. Tune the resolutions in the 1st and 2nd pass fitting in the config file if necessary. $META_PATH is the json file created to store the list of meshes paths.

Now convert the DMTet dataset (stored as python dicts) into a dataset of 3D cubic grids:

cd ../data/
python tets_to_3dgrid.py --resolution $RESOLUTION --root $DMTET_DICT_FOLDER --source $SOURCE_FOLDER --target grid --index 0

in which we assume the DMTet dict dataset is stored in $DMTET_DICT_FOLDER/$SOURCE_FOLDER and we will save the resulted cubic grid dataset in $DMTET_DICT_FOLDER/grid.

Create a meta file of all dmtet 3D cubic grid file locations for diffusion model training:

cd ../metadata/
python save_meta.py --data_path $DMTET_DATA_PATH/tets --json_path $META_FILE

Train a diffusion model

cd ..

python main_diffusion.py --mode=train --config=$DIFFUSION_CONFIG \
--config.data.meta_path=$META_FILE \
--config.data.filter_meta_path=$TRAIN_SPLIT_FILE

where $TRAIN_SPLIT_FILE is a json list of indices to be included in the training set. Examples in metadata/train_split/. For the diffusion model config file, please refer to configs/res64.py or configs/res128.py.

Texture Generation

Follow the instructions in https://github.com/TEXTurePaper/TEXTurePaper and create text-conditioned textures for the generated meshes.

Others

If tetrahedral grids of higher resolutions are needed, first follow the README in nvdiffrec/data/tets and use quartet to generate a uniform tetrahedral grid. Then run nvdiffrec/data/tets/crop_tets.py to remove the boundary (so that translational symmetry holds in the resulted grid).

Blender Visualization

To visualize generated meshes with blender, please see blender_viz/ for more details.

Citation

If you find our work useful to your research, please consider citing:

@InProceedings{Liu2023MeshDiffusion,
    title={MeshDiffusion: Score-based Generative 3D Mesh Modeling},
    author={Zhen Liu and Yao Feng and Michael J. Black and Derek Nowrouzezahrai and Liam Paull and Weiyang Liu},
    booktitle={International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=0cpM2ApF9p6}
}

Acknowledgement

This repo is adapted from https://github.com/NVlabs/nvdiffrec and https://github.com/yang-song/score_sde_pytorch.