Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumption that data lives in a Euclidean grid. In this paper we introduce Diffusion Probabilistic Fields (DPF), a diffusion model that can learn distributions over continuous functions defined over metric spaces, commonly known as fields. We extend the formulation of diffusion probabilistic models to deal with this field parametrization in an explicit way, enabling us to define an end-to-end learning algorithm that side-steps the requirement of representing fields with latent vectors as in previous approaches (Dupont et al., 2022a; Du et al., 2021). We empirically show that, while using the same denoising network, DPF effectively deals with different modalities like 2D images and 3D geometry, in addition to modeling distributions over fields defined on non-Euclidean metric spaces.
翻译:扩散概率模型很快成为图像、3D几何学、视频和其他领域的基因模型的主要模型。然而,为了将扩散性基因模型应用到这些领域,拆离网络需要为每个领域独立地、往往在假设数据存在于Euclidean网格中的情况下,对每个领域进行仔细设计。在本文件中,我们引入了Diful化概率模型(DPF),这种模型可以了解在公用空间(通常称为字段)上定义的连续功能的分布。我们扩展了扩散性模型的配方,以便以明确的方式处理这个领域对齐,使我们能够界定一种端到端的学习算法,这种算法将代表潜在矢量的字段的要求与以前的方法一样(Dopont等人,2022a;Du等人,2021年)。我们从经验中表明,除了在使用相同的解析网络的同时,DPF有效处理2D图像和3D几何测量等不同模式,此外还在非Euclidean测量空间上定义的字段上进行建模。</s>