Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

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.

Environment setup

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

Demo

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

    

    

    

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