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.

翻译：近期研究提出了一类新型生成模型，用于合成隐式神经表征（INRs），以捕获不同域中的任意连续信号。这些模型为域无关生成模型开辟了道路，但往往难以实现高质量生成。我们发现现有方法通过生成神经网络权重参数化INRs，并使用固定位置嵌入（PEs）评估网络。这种架构限制了生成模型的表达能力，导致INR生成质量低下。为解决此局限，我们提出面向INRs的域无关潜扩散模型（DDMI），该方法生成自适应位置嵌入而非神经网络权重。具体而言，我们开发了离散-连续空间变分自编码器（D2C-VAE），在共享潜空间中无缝连接离散数据与连续信号函数。此外，我们引入新型条件机制，通过分层分解的位置嵌入评估INRs，进一步增强表达能力。在四种模态（包括二维图像、三维形状、神经辐射场与视频）的七个基准数据集上的大量实验表明，DDMI具有广泛适用性，且性能优于现有INR生成模型。