MeshDiffusion: Score-based Generative 3D Mesh Modeling (ICLR 2023 Spotlight)

This is the official implementation of MeshDiffusion.

MeshDiffusion is a diffusion model for generating 3D meshes with a direct parametrization of deep marching tetrahedra (DMTet). Please refer to https://meshdiffusion.github.io for more details.

Getting Started

Requirements

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

Follow the instructions to install requirements for nvdiffrec: https://github.com/NVlabs/nvdiffrec

Pretrained Models

Download the model checkpoints from https://drive.google.com/drive/folders/15IjbUM1tQf8gS0YsRqY5ZbMs-leJgoJ0?usp=sharing.

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 --sample-path $SAMPLE_PATH [--deform-scale $DEFORM_SCALE]

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).

Single-view Conditional Generation

First fit a DMTet from a single view of a mesh

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

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 --out-dir $DMTET_DATA_PATH

Create a meta file of all dmtet 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/.

Texture Completion

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

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.