TopoFlowForge: Progressive Native Mesh Generation via Cascaded Flow Matching

3D content generation technology has significantly advanced the work of designers, as well as the 3D printing and gaming industries. However, it remains difficultto produce lightweight, editable, and topologically clean artistic content that isdirectly production-ready. To achieve this, we present TopoFlowForge, an artistic mesh foundation model that generates production-ready meshes. Specifically,TopoFlowForge decomposes the mesh generation process into vertices generationand their connectivity prediction, i.e., edges. We formulate vertices generationas a two-stage coarse-to-fine process and incorporate several effective loss functions to further enhance its performance. In the connectivity prediction stage, wepropose a simple yet effective method for estimating the connectivity affinity between vertices and additionally predict per-vertex normals, which determines thecorrect orientation of faces. Besides, we construct a large-scale dataset combininghand-crafted 3D assets with public high-quality topology datasets. Based on this, a carefully designed data curation pipeline is employed to filter the raw dataset,retaining only high-quality topology data for model training. Our model is trainedon the combined dataset and tested on both out-of-distribution hand-crafted set of3D assets and public datasets. Under image conditioning, TopoFlowForge surpasses autoregressive methods in both generation quality and inference speed,achieving state-of-the-art results among open-source mesh topology generators. We will release all code and weights together with a portion of our test dataset.