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: this https URL

Shape Non-rigid Kinematics (SNK) 是一种新的零样本非刚性形状匹配方法，无需大量训练或基准数据，通过使用编码器 - 解码器架构对单一形状对进行基于重建的策略变形，预测出无监督的功能映射并转换为点对点映射，以实现重建的监督机制，我们的研究在传统基准测试中表现出色，简化了形状匹配流程，而不降低准确性，我们的代码可以在线找到：this https URL