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English(EN) Coarse3D: Controllable and Faithful Sparse Structure Generation for Image-to-3D

Coarse3D 模型可从图像生成可控且忠实的稀疏 3D 结构

研究人员推出 Coarse3D,这是一种新颖的模型,旨在从单个图像生成可控且忠实的稀疏 3D 结构。该方法解决了现有粗略阶段生成器在方向控制的模糊语义规范化和由于薄弱的 2D-3D 基础导致结构不一致等方面的局限性。Coarse3D 采用第一帧锚定的规范空间、具有辅助重建的双塔扩散 Transformer (DiT) 以及空间先验引导的注意力,以提高方向控制和结构一致性。该模型可以集成到现有流程中,通过提高最终 3D 生成质量来展示最先进的性能。 AI

影响 引入了一种改进图像到 3D 生成初始阶段的新方法,有望提高 3D 资产创建的质量和可控性。

排序理由 详细介绍新模型及其技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Coarse3D 模型可从图像生成可控且忠实的稀疏 3D 结构

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详细介绍新模型及其技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Gaige Wang, Bowen Pan, Hao Ren, Jiangjing Lyu, Chengfei Lyu ·

    Coarse3D: Controllable and Faithful Sparse Structure Generation for Image-to-3D

    arXiv:2610.41231v1 Announce Type: new Abstract: Coarse-to-fine 3D-native generation relies heavily on the first-stage sparse structure, which determines the global orientation and overall geometry for subsequent refinement. However, existing coarse generators suffer from two key …