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DeepMetaHandles is a shape deformation technique. It learns a set of meta-handles for each given shape. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation direction. A new deformation can then be generated by the "linear combination" of the meta-handles. Although the approach is learned in an unsupervised manner, the learned meta-handles possess strong interpretability and consistency.

1. Create a conda environment by conda env create -f environment.yml.
2. Build and install torch-batch-svd.

1. Download data/demo and checkpoints/chair_15.pth from here and place them in the corresponding folder. Pre-processed demo data contains the manifold mesh, sampled control point, sampled surface point cloud, and corresponding biharmonic coordinates.
2. Run src/demo_target_driven_deform.py to deform a source shape to match a target shape.
3. Run src/demo_meta_handle.py to generate deformations along the direction of each learned meta-handle.

1. Download data/chair from here and place them in the corresponding folder.
2. Run the visdom server. (We use visdom to visualize the training process.)
3. Run src/train.py to start training.

Note: For different categories, you may need to adjust the number of meta-handles. Also, you need to tune the weights for the loss functions. Different sets of weights may produce significantly different results.

1. Compile codes in data_preprocessing/.
2. Build and run manifold to convert your meshes into watertight manifolds.
3. Run data_preprocessing/normalize_bin to normalize the manifold into a unit bounding sphere.
4. Build and run fTetWild to convert your manifolds into tetrahedral meshes. Please use --output xxx.mesh option to generate the .mesh format tet mesh. Also, you will get a xxx.mesh__sf.obj for the surface mesh. We will use xxx.mesh and xxx.mesh__sf.obj to calculate the biharmonic weights. We will only deform xxx.mesh__sf.obj later.
5. Run data_preprocessing/sample_key_points_bin to sample control points from xxx.mesh__sf.obj. We use the FPS algorithm over edge distances to sample the control points.
6. Run data_preprocessing/calc_weight_bin to calculate the bihrnomic weights. It takes xxx.mesh, xxx.mesh__sf.obj, and the control point file as input, and will output a text file containing the weight matrix for the vertices in xxx.mesh__sf.obj.
7. Run data_preprocessing/sample_surface_points_bin to sample points on the xxx.mesh__sf.obj and calculate the corresponding biharmonic weights for the sampled point cloud.
8. In our training, we remove those shapes (about 10%) whose biharmonic weight matrix contains elements that are smaller than -1.5 or greater than 1.5. We find that this can help us to converge faster.
9. To reduce IO time during training, you may compress the data into a compact form and load them to the memory. For example, you can use python scripts in data_preprocessing/merge_data to convert cpp output into numpy files.

If you find our work useful, please consider citing our paper:

@article{liu2021deepmetahandles,
  title={DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates},
  author={Liu, Minghua and Sung, Minhyuk and Mech, Radomir and Su, Hao},
  journal={arXiv preprint arXiv:2102.09105},
  year={2021}
}