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English(EN) ArtCrafter: Feed-Forward Generation of Articulated 3D Object with Analytic Joint Derivation

ArtCrafter 使用 Diffusion Transformer 从图像生成铰接式3D对象

研究人员开发了 ArtCrafter,这是一种新颖的前馈生成模型,能够仅从两张图像创建铰接式3D对象。该模型使用具有独特槽布局的 Diffusion Transformer 架构,在单次传递中联合生成静止和开放铰接状态的网格。ArtCrafter 通过专门的注意力机制和排斥损失来确保几何一致性并防止部件穿透,从而解析 URDF 关节参数以获得可解释的结果。实验表明,ArtCrafter 在几何质量和铰接精度方面优于现有方法。 AI

影响 该模型通过从简单的图像输入实现更精确和一致的生成,有望推动机器人和仿真3D资产的创建。

排序理由 该集群描述了 arXiv 论文中提出的一种新颖的生成模型。[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 →

ArtCrafter 使用 Diffusion Transformer 从图像生成铰接式3D对象

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该集群描述了 arXiv 论文中提出的一种新颖的生成模型。[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) · Minh Canh Tu, Quang-Binh Nguyen, Khoi Nguyen ·

    ArtCrafter:具有解析关节推导的铰接式3D对象的馈入式生成

    arXiv:2611.07658v1 Announce Type: new Abstract: Articulated 3D assets are essential for robotic simulation and training, yet their generation from casual images poses a key challenge: recovering part geometry and kinematics that remain mutually consistent. We present ArtCrafter, …