# State of the Art on Diffusion Models for Visual Computing

**Authors:** Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Eric Ryan Chan, Tali Dekel, Aleksander Holynski, Angjoo Kanazawa, C. Karen Liu, Lingjie Liu, Ben Mildenhall, Matthias Nießner, Björn Ommer, Christian Theobalt, Peter Wonka, Gordon Wetzstein  
**arXiv:** [2310.07204](https://arxiv.org/abs/2310.07204)  
**Date:** 2023-10-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.07204v1/

## Key points

- The report focuses on visual computing and generative AI advancements
- Diffusion models are preferred for generative AI in image, video, and 3D scene generation
- Rapid growth in literature on diffusion-based tools and applications
- Intuitive starting point for researchers, artists, and practitioners interested in diffusion models
- Introduction to mathematical concepts and implementation details of Stable Diffusion model
- Coverage of personalization, conditioning, inversion, and other important aspects
- Comprehensive overview of literature categorized by generated medium (2D images, videos, 3D objects)
- Discussion of datasets, metrics, open challenges, and social implications
- Highlighting DreamFusion as an example of using pre-trained image diffusion priors for text-to-3D generation
- Considerations related to explainability, trustworthiness, and accountability when working with generative AI models

## Summary

This state-of-the-art report (STAR) focuses on the field of visual computing and the advancements brought about by generative artificial intelligence (AI). Specifically, it explores diffusion models as the preferred architecture for generative AI in the domains of image, video, and 3D scene generation, editing, and reconstruction. The report highlights the exponential growth in literature on diffusion-based tools and applications within the past year. Papers on this topic are being published across various communities including computer graphics, computer vision, and AI. The rapid expansion of this field makes it challenging to keep up with all recent developments. The main objective of this STAR is to provide an intuitive starting point for researchers, artists, and practitioners interested in exploring diffusion models. It begins by introducing the basic mathematical concepts behind these models and delves into implementation details and design choices of the popular Stable Diffusion model. Additionally, it covers important aspects such as personalization, conditioning, inversion among others. A comprehensive overview is provided on the rapidly growing literature related to diffusion-based generation and editing. This literature is categorized based on the type of generated medium including 2D images, videos, 3D objects locomotion and 4D scenes. Furthermore available datasets metrics open challenges and social implications are discussed. In section 6.2.1 titled "Methods," DreamFusion is highlighted as a prime example of utilizing pre-trained image diffusion priors for text-to-3D generation. This innovative approach has achieved groundbreaking results in generating realistic 3D models from text prompts. Section 23 discusses important considerations related to explainability trustworthiness and accountability when working with generative AI models.