Denoising Functional Maps: Diffusion Models for Shape Correspondence

Figure 1. We propose DenoisFM, a novel method for predicting shape correspondences in the form of functional maps using denoising diffusion models. (Left:) Challenging examples our method can handle, with color-coded correspondences. (Right:) By sequentially denoising samples of random noise, the diffusion model can predict the correct functional map between a pair of shapes.