用神经网络压缩动态网格序列，提升压缩效率和实时解码能力。
N4MC: Neural 4D Mesh Compression
- 将不规则网格序列转为规则4D张量，统一表示时空信息。
- 在相同质量下，比特率比现有方法降低15%以上。
- 适合需要高效传输动态3D模型的应用场景。
我们提出N4MC，首个基于神经网络的4D网格压缩框架，通过利用时序冗余高效压缩随时间变化的网格序列。与以往独立处理每帧网格的方法不同，N4MC借鉴2D视频编码中的帧间压缩思想，学习长序列中的运动补偿。具体而言，N4MC将连续的不规则网格帧转换为规则4D张量，实现统一且紧凑的表示。这些张量随后通过自编码器压缩，捕捉空间与时间相关性以去除冗余。为增强时序一致性，引入基于Transformer的插值模型，根据追踪体积中心的隐式嵌入预测中间网格帧，消除运动模糊。大量实验表明，N4MC在率失真性能上超越当前最优方法，并支持4D网格序列的实时解码。代码已公开于：https://github.com/frozzzen3/N4MC。
原文摘要 · Abstract (English)
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.
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