Implicit Neural Surface Deformation with Explicit Velocity Fields
Keywords: Implicit Shape Representation, Neural Implicit Representation, Shape Deformation, Neural Surfaces
TL;DR: We propose a novel method that can simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds, and reconstruct physically plausible intermediate shapes without supervision.
Abstract: In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propose to model the point movement using an explicit velocity field and directly deform a time-varying implicit field using the modified level-set equation. This equation utilizes an iso-surface evolution with Eikonal constraints in a compact formulation, ensuring the integrity of the signed distance field. By applying a smooth, volume-preserving constraint to the velocity field, our method successfully recovers physically plausible intermediate shapes. Our method is able to handle both rigid and non-rigid deformations without any intermediate shape supervision. Our experimental results demonstrate that our method significantly outperforms existing works, delivering superior results in both quality and efficiency.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 4738
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