SuperVoxelGPT: Adaptive and Ordered 3D Tokenization for Autoregressive Shape Generation

Autoregressive multimodal large language models (MLLMs)enable 3D generation but struggle to scale to high-resolution shapesdue to inadequate 3D tokenizations. Compact set-based representationsdiscard deterministic spatial ordering, leading to ambiguous sequenceprediction, while uniform or octree-based voxel grids preserve orderingat the cost of severe redundancy and excessively long sequences. Thisstructural trade-off limits stable and efficient autoregressive 3D gener-ation. We present SuperVoxelGPT, a representation-first frameworkthat resolves this tension through adaptive and deterministically orderedsupervoxel tokenization. Given a prompt, we first predict a coarse geo-metric saliency distribution and construct a shape-adaptive supervoxelpartition using saliency-guided centroidal Voronoi tessellation, allocatingfine-grained cells to complex regions and larger cells to smooth regions.Conditioned on this prompt and ordered supervoxel layout, we introducea SuperVoxelVAE and fine-tune a pretrained MLLM to autoregressivelygenerate supervoxel tokens. Experiments using Trellis-500K data showthat SuperVoxelGPT reduces token sequence length to 12.8% of uniformvoxel tokenization while achieving state-of-the-art generation quality andan average 10× speedup over prior methods.