Recent studies have introduced a new class of generative models for
synthesizing implicit neural representations (INRs) that capture arbitrary
continuous signals in various domains. These models opened the door for
domain-agnostic generative models, but they often fail to achieve high-quality
generation. We observed that the existing methods generate the weights of
neural networks to parameterize INRs and evaluate the network with fixed
positional embeddings (PEs). Arguably, this architecture limits the expressive
power of generative models and results in low-quality INR generation. To
address this limitation, we propose Domain-agnostic Latent Diffusion Model for
INRs (DDMI) that generates adaptive positional embeddings instead of neural
networks' weights. Specifically, we develop a Discrete-to-continuous space
Variational AutoEncoder (D2C-VAE), which seamlessly connects discrete data and
the continuous signal functions in the shared latent space. Additionally, we
introduce a novel conditioning mechanism for evaluating INRs with the
hierarchically decomposed PEs to further enhance expressive power. Extensive
experiments across four modalities, e.g., 2D images, 3D shapes, Neural Radiance
Fields, and videos, with seven benchmark datasets, demonstrate the versatility
of DDMI and its superior performance compared to the existing INR generative
models.

我们提出了用于隐式神经表示的无领域隐性扩散模型 (Domain-agnostic Latent Diffusion Model)，该模型生成自适应位置嵌入，而不是神经网络的权重。我们还引入了一种用于评估隐式神经表示的新型条件机制，通过分层分解的位置嵌入进一步增强了表达能力。在四种领域（2D 图像、3D 形状、神经辐射场和视频）上进行的广泛实验，使用了七个基准数据集，展示了 DDMI 的多功能性和优越的性能，相比现有的隐式神经表示生成模型。