Challenges exist for the effective representation of 3D meshes due to their complexity and irregularity.To address the limitations of conventional graph convolution in propagating and integrating information across 3 D meshes,this paper proposes a 3 D mesh model based on variational autoencoders with vector quantization to explore their latent space for 3D mesh generation.The introduction of residual graph convolution modules,specifically designed for intricate graph structures like triangular meshes,enhances the integration of multi-layered feature information through residual connections,supporting deeper network architectures and significantly improving model performance and generalization.Building upon a reliable edge contraction algorithm for mesh simplification,a hierarchical structure is encoded through robust multi-level pooling and unpooling operations,effectively capturing correspondences between coarser and denser meshes.Meanwhile,in the process of projecting 3D mesh shapes into the latent space,potential feature compression leading to information loss is addressed by employing vector quantization to map latent features to predefined discrete vectors.Our experimental results demonstrate the proposed algorithm learns compact representations for deformable shape collections,delivering outstanding performances in various applications such as shape generation and interpolation.
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