This video presents our tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications. This tutorial was originally presented at CVPR 2022 in New Orleans and it received a lot of interest from the research community. After the conference, we decided to record the tutorial again and broadly share it with the research community. We hope that this video can help you start your journey in diffusion models. Visit this page for the slides and more information: https://cvpr2022-tutorial-diffusion-models.github.io/ Outline: 0:00:00 Introduction (Arash) 0:08:17 Part 1: Denoising Diffusion Probabilistic Models (Arash) 0:52:14 Part 2: Score-based Generative Modeling with Differential Equations (Karsten) 1:47:40 Part 3: Advanced Techniques: Accelerated Sampling, Conditional Generation (Ruiqi) 2:37:39 Applications 1: Image Synthesis, Text-to-Image, Semantic Generation (Ruiqi) 2:58:29 Applications 2: Image Editing, Image-to-Image, Superresolution, Segmentation (Arash) 3:20:42 Applications 3: Discrete State Models, Medical Imaging, 3D & Video Generation (Karsten) 3:35:20 Conclusions, Open Problems, and Final Remarks (Arash) Follow us on Twitter: Karsten Kreis: https://twitter.com/karsten_kreis Ruiqi Gao: https://twitter.com/RuiqiGao Arash Vahdat: https://twitter.com/ArashVahdat #CVPR2022 #generative_learning #diffusion_models #tutorial #ai #research