OREO: Fidelity Alignment in 3D Generation via On-the-fly Rendering-Editing Optimization

Despite recent advancements in 3D generation, models oftenstruggle to produce assets with high visual fidelity. To bridge this gap,we propose OREO, an alignment framework that enhances the realismof 3D generators by leveraging rich 2D diffusion priors. Instead of relyingon static datasets, OREO establishes a dynamic optimization loop thatproduces on-the-fly pseudo ground truths. At its core, OREO combinestwo components: (i) Reinforced Editing, which uses an image editor torefine rendered views of the 3D output, improving the visual fidelity ofrendered views while preserving geometric structure; and (ii) ContrastiveDistillation, which treats the edited views as positive and the originalrenderings as negative anchors, distilling the fidelity gap into the 3Dgenerator via a latent contrastive objective. Experiments demonstratethat OREO effectively improves upon pre-trained baselines, producing3D assets with enhanced visual realism. Our project page is at https://theericma.github.io/oreo/.