Generative industrial design



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.

References

Deitke, Matt, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. 2022. β€œObjaverse: A Universe of Annotated 3D Objects.” arXiv.
Du, Yilun, Katherine M. Collins, Joshua B. Tenenbaum, and Vincent Sitzmann. 2021. β€œLearning Signal-Agnostic Manifolds of Neural Fields.” In Advances in Neural Information Processing Systems.
Dupont, Emilien, Hyunjik Kim, S. M. Ali Eslami, Danilo Jimenez Rezende, and Dan Rosenbaum. 2022. β€œFrom Data to Functa: Your Data Point Is a Function and You Can Treat It Like One.” In Proceedings of the 39th International Conference on Machine Learning, 5694–5725. PMLR.
Zhuang, Peiye, Samira Abnar, Jiatao Gu, Alex Schwing, Joshua M. Susskind, and Miguel Ángel Bautista. 2022. β€œDiffusion Probabilistic Fields.” In.

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