Deformation Recovery: Localized Learning for Detail-Preserving Deformations

Abstract

We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that require a global shape encoding, we observe that detail-preserving deformations can be estimated reliably without any global context in certain scenarios. Building on this intuition, we leverage Jacobians defined in a one-ring neighborhood as a coarse representation of the deformation. Using this as the input to our neural network, we apply a series of MLPs combined with feature smoothing to learn the Jacobian corresponding to the detail-preserving deformation, from which the embedding is recovered by the standard Poisson solve. Crucially, by removing the dependence on a global encoding, every \textit{point} becomes a training example, making the supervision particularly lightweight. Moreover, when trained on a class of shapes, our approach demonstrates remarkable generalization across different object categories. Equipped with this novel network, we explore three main tasks: refining an approximate shape correspondence, unsupervised deformation and mapping, and shape editing. Our code is made available at https://github.com/sentient07/LJN

References in corpus (8)

• Point-Set Registration: Coherent Point Drift
• Learning from Synthetic Humans
• MeshCNN: A Network with an Edge
• PCPNET: Learning Local Shape Properties from Raw Point Clouds
• Unsupervised Learning of Robust Spectral Shape Matching
• Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
• Variational Barycentric Coordinates
• Explorable Mesh Deformation Subspaces from Unstructured Generative Models