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English(EN) DiffGI: Differentiable Geometry Images for High-Fidelity Thin-Shell 3D Generation

DiffGI 引入微分几何图像以实现高保真三维生成

研究人员推出了一种新颖的三维生成框架 DiffGI,该框架利用微分几何图像。该方法用连续的二维截断符号距离函数 (TSDF) 取代了传统的体积表示,以克服表示薄壳和非流形几何体的局限性。DiffGI 通过集成微分 Marching Squares 算法实现了端到端训练,允许梯度从三维表面损失反向传播到二维潜在空间。该框架包含一个用于压缩三维表面的 DiffGI-VAE 和一个基于 Transformer 的潜在扩散模型,用于条件生成,并在实验中展示了卓越的保真度和效率。 AI

影响 这种新方法能够更精确、更高效地生成复杂的三维模型,特别适用于涉及服装等薄壳结构的应用程序。

排序理由 该集群包含一篇详细介绍三维生成新方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

DiffGI 引入微分几何图像以实现高保真三维生成

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该集群包含一篇详细介绍三维生成新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Eungjune Shim, Hansol Lee, Eunjung Ju ·

    DiffGI:用于高保真薄壳三维生成的微分几何图像

    arXiv:2607.13365v1 Announce Type: new Abstract: Existing 3D generative models predominantly rely on implicit volumetric representations, which enforce watertight topology and struggle to represent thin-shell and non-manifold geometries such as garments. Geometry image-based appro…

  2. arXiv cs.CV TIER_1 English(EN) · Eunjung Ju ·

    DiffGI:用于高保真薄壳三维生成的微分几何图像

    Existing 3D generative models predominantly rely on implicit volumetric representations, which enforce watertight topology and struggle to represent thin-shell and non-manifold geometries such as garments. Geometry image-based approaches offer a surface-centric alternative, but e…