隐式神经场通常由多层感知器（MLP）编码，从坐标（如xyz）映射到信号（如符号距离），已经表现出高保真度和紧凑性的优越性能。然而，隐式神经场缺乏规则和明确的网格结构，使得直接在隐式神经场上应用生成建模以合成新数据变得具有挑战性。因此，我们提出了HyperDiffusion，这是一种针对隐式神经场的无条件生成建模的新方法。HyperDiffusion直接在MLP权重上操作，并使用合成的MLP参数编码生成新的神经隐式场。具体而言，首先优化一组MLP，以忠实地表示单个数据样本。随后，在这个MLP权重空间中，训练扩散过程来对神经隐式场的潜在分布进行建模。HyperDiffusion使得扩散建模跨越了一个隐式、紧凑、但高保真度的表示，这个表示可以横跨三维形状和四维网格动画，并在一个统一的框架中实现。
标题：HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion
作者：
项目：https://ziyaerkoc.com/hyperdiffusion/
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