Modal Space Subdivision for Physically-plausible 4D Shape Sequence Completion from Sparse Samples

Abstract

Subdivision techniques are powerful and ubiquitous in generating smooth surfaces from coarse 3D polygonal models, yet space-time subdivision remains unexplored when the goal is to produce dense physically-realistic shape sequence with smooth transition from a set of sparse 3D model samples. In this paper, we considers a completely data-driven strategy and proposes a novel modal space subdivision scheme to facilitate the rapid generation of physically-plausible "in-between" shapes from sparse model samples. The key idea of our approach is to abstract any shape as a point in modal space, represent its potential deformation with shape modes, and iteratively seek all the intermediate shapes between two consecutive models in the modal space via recursive subdivision. Meanwhile, to guarantee deformation details, we further interpolate the shape in the local regions after global modal-space subdivision. Comprehensive experiments on various model inputs have demonstrated the power, versatility, high performance, and potential of our modal space subdivision scheme.