We　tackle　the　problem　of　single-shape　3D　generation,　aiming　to　synthesize　diverse　and　plausible　shapes　conditioned　on　a　single　input　exemplar.　This　task　is　challenging　due　to　the　absence　of　dataset-level　variation,　requiring　models　to　internalize　structural　patterns　and　generate　novel　shapes　from　limited　local　geometric　cues.　To　address　this,　we　propose　a　unified　framework　combining　geometry-aware　representation　learning　with　a　multiscale　diffusion　process.　Our　approach　centers　on　a　triplane　autoencoder　enhanced　with　a　spatial　pattern　predictor　and　attention-based　feature　fusion,　enabling　fine-grained　perception　of　local　structures.　To　preserve　structural　coherence　during　generation,　we　introduce　a　soft　feature　distribution　alignment　loss　that　aligns　features　between　input　and　generated　shapes,　balancing　fidelity　and　diversity.　Finally,　we　adopt　a　hierarchical　diffusion　strategy　that　progressively　refines　triplane　features　from　coarse　to　fine,　stabilizing　training　and　improving　quality.　Extensive　experiments　demonstrate　that　our　method　produces　high-fidelity,　structurally　consistent,　and　diverse　shapes,　establishing　a　strong　baseline　for　single-shape　generation.