High-fidelity Compression of Dynamic Meshes with Fine Details using Piece-wise Manifold Harmonic Bases

Abstract

Mesh-based animation, usually represented as dynamic meshes with fixed connectivity, is becoming more and more prevalent in movies, games and other graphics applications nowadays, and there is a growing need to compactly store and rapidly transmit these meshes for practical use, especially for those with high-quality geometric details. In this paper, we explore a novel key-frame based dynamic mesh compression method, wherein we apply pose-similarity with spectral techniques to define piece-wise manifold harmonic bases to reduce spatial-temporal redundancy. We first partition the sequence into several clusters with similar poses, and then decompose the meshes in each cluster into primary poses and geometric details using the manifold harmonic bases derived from the extracted key-frame in that cluster. The primary poses can be characterized as linear combinations of manifold harmonic bases, and the geometric details can be recovered by deformation transfer technique. Thus, we only need a small number of key-frames and a few coefficients for compressing dynamic meshes, which saves a significant amount of storage comparing to traditional methods in which bases are stored explicitly. Furthermore, we apply a second-order linear prediction coding to the harmonic coefficients to further reduce the temporal redundancy. Our extensive experiments and evaluations on various datasets have manifested that our novel method could obtain a high compression ratio while preserving high-fidelity geometry details and guaranteeing limited human perceived distortion rate simultaneously.