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

翻译：我们提出了形状非刚性运动学（SNK），一种用于非刚性形状匹配的新型零样本方法，消除了对大量训练或真实数据的需求。SNK 在单对形状上运行，采用基于重建的策略，利用编码器-解码器架构将源形状变形以紧密匹配目标形状。在此过程中，预测一个无监督功能图并将其转化为点对点映射，作为重建的监督机制。为辅助训练，我们设计了一种新的解码器架构，生成平滑、逼真的变形。SNK 在传统基准测试中展现出有竞争力的结果，简化了形状匹配过程且不牺牲精度。我们的代码可在网上找到：https://github.com/pvnieo/SNK