Multi-Descriptor Mesh Animation Compression

Abstract

For mesh animation sequences with consistent topology, traditional compression methods are well established, but deep learning approaches are emerging. This paper presents a novel deep learning framework for vertex position reconstruction by encoding displacements. We propose LoD-Uniform Partitioning (LUP), which uses Levels-of-Detail (LoD) partitioning and uniform sampling to partition vertices into base and inference layers. Base layers use vertex-wise prediction and run-length coding, while inference layers use inter-layer prediction and a deep entropy model with a permutation-invariant transformer. By integrating vertex-based, face-based, and trajectory-based descriptors into our model context, we improve coding efficiency. Experiments on mesh animation datasets show that our method achieves a 25% bitrate reduction for lossless compression, a 15% BD-Rate improvement, and a 95% reduction in encoding time for lossy compression compared to MPEG Video-based Dynamic Mesh Compression (V-DMC), underscoring its strong potential for practical applications.