MOC-3D: Manifold-Order Consistency for Text-to-3D Generation
With the burgeoning development of fields such as the Metaverse, Virtual Reality (VR), and Digital Twins, text-to-3D generation has emerged as a research hotspot in both academia and industry. Currently, optimization methods based on Score Distillation Sampling (SDS) utilizing 2D diffusion priors have become the mainstream technological paradigm in this field. However, due to the view bias of 2D priors and the mode-seeking ambiguity combined with gradient noise induced by high Classifier-Free Guidance (CFG), these methods still suffer from macro-topological inconsistency (e.g., the Janus problem) and micro-geometric discontinuity. To address these challenges, we propose MOC-3D, a text-to-...
| Authors | Chenyang Fan, Junshi Cheng, Wen Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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