N4MC: Neural 4D Mesh Compression

2026-02-23

AI Summary

This work proposes the first neural compression framework tailored for time-varying 4D mesh sequences, addressing the limitation of existing methods that overlook inter-frame redundancy and struggle to achieve efficient compression. Inspired by inter-frame coding in 2D video compression, the approach introduces a Transformer-based interpolation model coupled with a barycentric tracking mechanism to effectively mitigate motion blur and enhance temporal consistency. By representing irregular mesh sequences as unified 4D tensors and integrating a self-decoder architecture with latent variable embeddings, the method jointly models spatiotemporal correlations and motion dynamics. Experimental results demonstrate that the proposed framework significantly outperforms state-of-the-art methods in rate-distortion performance while enabling real-time decoding.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

We present N4MC, the first 4D neural compression framework to efficiently compress time-varying mesh sequences by exploiting their temporal redundancy. Unlike prior neural mesh compression methods that treat each mesh frame independently, N4MC takes inspiration from inter-frame compression in 2D video codecs, and learns motion compensation in long mesh sequences. Specifically, N4MC converts consecutive irregular mesh frames into regular 4D tensors to provide a uniform and compact representation. These tensors are then condensed using an auto-decoder, which captures both spatial and temporal correlations for redundancy removal. To enhance temporal coherence, we introduce a transformer-based interpolation model that predicts intermediate mesh frames conditioned on latent embeddings derived from tracked volume centers, eliminating motion ambiguities. Extensive evaluations show that N4MC outperforms state-of-the-art in rate-distortion performance, while enabling real-time decoding of 4D mesh sequences. The implementation of our method is available at: https://github.com/frozzzen3/N4MC.

Problem

• 4D mesh compression
• temporal redundancy
• time-varying meshes
• neural compression
• mesh sequences

Innovation

• neural compression
• 4D mesh
• motion compensation
• temporal redundancy
• transformer-based interpolation