GrowFlow: Unified 3D Structure and Feature Generation via Expanding Structural Flow Matching

Structured latent representations have been widely adopted for efficient 3D shape modeling and generation in recent years, due to their favorable trade-off between geometric fidelity and computational efficiency. However, most existing genera- tive approaches decompose the generation process into two stages: first predicting sparse voxel structure, and then generating latent features based on that struc- ture. Although effective, this staged design introduces extra parameters to predict the voxel structure. While the voxel structure does not require many parameters compared to the detailed geometry, because of the need for generalization, it is necessary to build a huge structure prediction network. To address this, we pro- pose GrowFlow, the first 3D generative framework to unify structure and feature generation across resolutions within a single flow-matching process. Specifically, we provide a principled mapping strategy that enables consistent flow matching across different resolutions and structural states, laying the foundation for multi- scale structural modeling. To avoid structure-feature decomposition, we integrate a dedicated structure generation module into the flow matching paradigm, which jointly models sparse voxel structure and fine-grained geometric features in an end-to-end manner. Using a Diffusion Transformer (DiT) as the backbone, our framework achieves generation quality comparable to the state-of-the-art while using fewer parameters than multi-stage pipelines and reducing the computation required. Extensive experiments on benchmark datasets show that GrowFlow in- fers 3D occupancy progressively from coarse to fine resolution, matching the ge- ometric fidelity of state-of-the-art methods while improving generation efficiency. Notably, our model enables resolution-adaptive, progressive generation, letting users preview low-resolution results, allowing early termination or full-generation.