Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape
  Matching via Unsupervised Functional Map Regularized Reconstruction
Souhaib Attaiki Maks Ovsjanikov
We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for
non-rigid shape matching that eliminates the need for extensive training or
ground truth data. SNK operates on a single pair of shapes, and employs a
reconstruction-based strategy using an encoder-decoder architecture, which
deforms the source shape to closely match the target shape. During the process,
an unsupervised functional map is predicted and converted into a point-to-point
map, serving as a supervisory mechanism for the reconstruction. To aid in
training, we have designed a new decoder architecture that generates smooth,
realistic deformations. SNK demonstrates competitive results on traditional
benchmarks, simplifying the shape-matching process without compromising
accuracy. Our code can be found online: https://github.com/pvnieo/SNK