Taming TRELLIS.2: Topology-Aware Online Post-Training for 3D Mesh Generation

Directly generating 3D assets is essential for scalable 3D content creation. Recent latent flow-based 3D generative models, such as TRELLIS.2, achieve impressive generation quality and efficiency by employing expressive 3D representations that support arbitrary topology. However, such topological flexibility comes at the cost of losing guarantees on topological regularity, potentially introducing unintended non-manifold structures when regular topology is desired. We propose TopoFT, a topology-aware fine-tuning method for online post-training of pretrained 3D generators that improves topological regularity without modifying the underlying O-Voxel representation or restricting its support for arbitrary topology. Our diagnostic analysis identifies Stage-2 shape-SLat generation as a major source of topology irregularity. Since topology is evaluated globally while individual irregularities are spatially localized, TopoFT decomposes each mesh-level group-relative advantage into additive token-wise attributions that exactly preserve the global advantage. An anchored dual-branch flow objective then reinforces favorable local trajectories while stably suppressing unfavorable ones. Across four unseen benchmarks, TopoFT consistently reduces non-manifold edges, non-manifold vertices, and boundary edges without systematic degradation in geometric fidelity. These improvements translate into substantial downstream benefits: the target-reaching rate under aggressive QEM simplification increases from 52.7–91.0% to 95.8–99.4%, while the open-wall path ratio in downstream slicing decreases from 15.7–22.9% to 8.0–14.5%.