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MOC-3D improves text-to-3D generation with manifold and view-order consistency

Researchers have introduced MOC-3D, a novel method for generating 3D models from text prompts. This approach addresses common issues in current text-to-3D generation techniques, such as topological inconsistencies and geometric discontinuities. MOC-3D utilizes a combination of semantic view-order consistency and manifold-based feature continuity to improve both the global structure and micro-details of the generated 3D objects. AI

影响 Introduces a new technique to improve the quality and consistency of 3D models generated from text, potentially advancing applications in virtual environments.

排序理由 This is a research paper detailing a new method for text-to-3D generation. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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MOC-3D improves text-to-3D generation with manifold and view-order consistency

报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Chenyang Fan, Junshi Cheng, Wen Yang, Zihong Li, Wenfeng Zhang, Wei Hu, Yi Zhang, Pan Zeng ·

    MOC-3D: Manifold-Order Consistency for Text-to-3D Generation

    arXiv:2605.01743v1 Announce Type: new Abstract: 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 o…