# Generative industrial design

September 16, 2022 — November 13, 2023

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## 1 Incoming

Baque et al. (2018)

Diffusion Probabilistic Fields - Apple Machine Learning Research

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. 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.

## 2 References

*Proceedings of the 35th International Conference on Machine Learning*.

*Advances in Neural Information Processing Systems*.

*Proceedings of the 39th International Conference on Machine Learning*.

*Computer Vision – ECCV 2022*. Lecture Notes in Computer Science.

*ACM Transactions on Graphics*.