Geometry-Informed Auxiliary Priors for Consistent 3D Content Generation

Despite recent advancements in single-view 3D re construction leveraging multi-view diffusion (MVD), they still suffer from multi-view inconsistency and low-fidelity reconstruction. Existing solutions often introduce priors through prolonged, large-scale training, which is costly, and they struggle with aligning features with conditioning signals. In this work, we propose GeometryAP, a training-free framework for consistent 3D content generation via geometry-informed auxiliary priors in a two-stage "generate-then-reconstruct" pipeline. Our approach consists of two critical components: (1) To enhance cross-view consistency, we design the Auxiliary Prior-guided Attention Mechanism (APAM), which uses the MVD's own pseudo-normal noise features as an auxiliary prior. APAM fuses this 3D prior information to activate crucial attention regions, ensuring the 3D consistency of generated multi-view images. (2) To achieve robust reconstruction from the generated multi view images, we propose the 3D Gaussian-based Global-to-Local Mesh optimizer (GaussGLMeshizer). It leverages 3DGS as an intermediate representation to generate normal maps from both auxiliary and basic views, which serve as auxiliary priors. The global optimizer uses normal maps from auxiliary views to ensure high-fidelity detail on unknown views, while the local optimizer refines existing details using basic view normals. Moreover, a top-k mechanism is applied at different stages to mitigate prior errors, further enhancing robustness. Experiments on single view inputs demonstrate that GeometryAP outperforms state of-the-art baselines in geometry consistency, detail integrity, and structure fidelity without data training.