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• The proposed skeletal latent diffusion framework achieves superior reconstruction quality compared to existing approaches.result 85%
• The method maintains higher computational efficiency compared with existing approaches.technical 80%
• The framework explicitly incorporates structural priors for efficient medical shape generation.method 90%
• The shape auto-encoder captures global geometric information through a differentiable skeletonization module.method 90%
• The decoder predicts implicit fields over sparsely sampled coordinates.method 90%
• New shapes are generated via a latent-space diffusion model followed by neural implicit decoding and mesh extraction.method 90%
• The method addresses the geometric complexity and topological variability challenges in anatomical shape generation.technical 85%
• A large-scale dataset called MedSDF was constructed to address limited medical shape data availability.method 90%

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