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LN3DIFF++:可扩展的潜伏神经场扩散,用于快速的3D生成.

Yushi Lan, Fangzhou Hong, Shangchen Zhou

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    此摘要是机器生成的。

    LN3DIFF++引入了一个新的框架,用于使用统一的3D扩散管道快速,高质量的3D生成. 这种方法可以实现多功能条件3D生成,优于现有的3D扩散技术.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 3D 图形 3D 图形

    背景情况:

    • 神经染已经取得了显著的进步,2D扩散模型显示成功.
    • 为高质量的3D生成提供统一的3D扩散管道仍然是一个挑战.

    研究的目的:

    • 为了介绍LN3DIFF++,这是一种用于快速,高质量和多功能条件3D生成的新框架.
    • 为了弥合统一的3D扩散管道的差距.

    主要方法:

    • 使用3D感知架构和变异自编码器 (VAE) 进行3D隐性空间编码.
    • 使用基于变压器的解码器来生成3D神经场.
    • 在3D意识的潜空间上训练一个扩散模型.

    主要成果:

    • 在ShapeNet和FFHQ上实现高级类别特定的3D生成.
    • 在Objaverse上实现无类别的图像/文本受条件的3D生成.
    • 在没有每实例优化的情况下,在推断速度方面超越现有的3D扩散方法.

    结论:

    • LN3DIFF++为条件3D生成提供了一个强大而高效的解决方案.
    • 该框架在不同的条件类型和数据集中展示了多功能性.
    • 为更快,更容易获得的3D内容创建铺平了道路.