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English(EN) Mesh-Agnostic Refinement Models for Latent Space Encoding of 3D Human Body Shapes

新框架可优化三维人体形状模型

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新框架，用于优化SMPL等参数化模型的三维人体形状重建。该框架结合了三种方法：用于校正潜在代码的Latent-VaRefine，用于使用DiffusionNet预测顶点位移的Vertex-VaRefine，以及用于直接表面变形优化的ElaOpt。这种方法显著减少了重建误差，并证明了其在SMPL-H和SMPL-X等不同人体模型上的适应性，以及对网格离散化和几何形状变化的鲁棒性。 AI

影响 提高了三维人体建模的几何保真度，可能有助于动画、虚拟现实和生物力学等领域的应用。

排序理由 该集群包含一篇学术论文，详细介绍了用于优化三维人体形状模型的新框架。

在 Hugging Face Daily Papers 阅读 →

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

新框架可优化三维人体形状模型

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该集群包含一篇学术论文，详细介绍了用于优化三维人体形状模型的新框架。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    用于三维人体形状潜在空间编码的网格无关精炼模型

    Parametric human body models such as the Skinned Multi-Person Linear (SMPL) model provide compact representations of human shape and pose, but their reconstructions remain affected by both errors in estimated latent parameters and limitations in the decoder's expressive capacity.…

  2. arXiv cs.CV TIER_1 English(EN) · Chaima Zouaoui, Jonathan Cerqueira, Sylvain Arguillere, Martin Bauer, Nicolas Charon, Mohamed Daoudi, Emmanuel Hartman ·

    用于三维人体形状潜在空间编码的网格无关精炼模型

    arXiv:2611.03285v2 Announce Type: replace Abstract: Parametric human body models such as the Skinned Multi-Person Linear (SMPL) model provide compact representations of human shape and pose, but their reconstructions remain affected by both errors in estimated latent parameters a…

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