Diffusion Models: The Next Big Thing in Visual Computing

This state-of-the-art report discusses the theory and practice of diffusion models for visual computing. Image credit

Overview

Visual computing, a realm that encapsulates the manipulation and generation of visual content, is undergoing a metamorphosis. Much like the printing press or the internet catalyzed revolutions in their respective eras, generative artificial intelligence (AI) is currently redefining visual computing. At the heart of this transformation lie diffusion models, AI architectures that are rapidly becoming the gold standard for creating and editing visual content.

A New Dawn for Visual Computing

The history of visual computing, encompassing fields such as computer graphics and 3D computer vision, is replete with efforts to develop models that can accurately replicate or infer physical attributes from images. This domain underpins various industries, from gaming and virtual reality to robotics and autonomous vehicles. But the rise of generative AI marks a seismic shift.

With tools like Stable Diffusion, Imagen, Midjourney, DALL-E 2, and DALL-E 3, AI can now generate and modify images, videos, or 3D objects with minimal input, often just a textual prompt or high-level guidance. These models, trained on vast datasets comprising billions of text-image pairs, encapsulate a wealth of knowledge in their billions of parameters. The result? A democratization of visual computing processes that traditionally required expert domain knowledge.

Diffusion Models: The Vanguards

At the forefront of this AI-led visual revolution are diffusion models. These models, often based on convolutional neural network (CNN) architectures, are adept at generating a wide range of visual content. And their prowess isn't limited to static 2D images. Advancements are allowing diffusion models to tackle higher-dimensional data, paving the way for innovations in video, 3D, and even 4D scene generation.

However, the journey hasn't been without challenges. While the internet brims with 2D images, there's a dearth of high-quality 3D or 4D content. Additionally, the computational demands of these models, coupled with their inherent iterative nature, mean they often require significant resources and time.

Yet, the progress is undeniable. The last year has witnessed a proliferation of research and applications centered around diffusion models, as illustrated by numerous examples in the visual computing domain.

Navigating the Diffusion Landscape

Given the rapid developments in diffusion models, there's a pressing need for structured overviews and comprehensive reports. This "State of the Art on Diffusion Models for Visual Computing" serves as a beacon, illuminating key concepts, applications, and challenges associated with these models. From introducing the foundational mathematics to exploring applications for 2D images, videos, 3D objects, and multi-view 4D scenes, this report provides invaluable insights. The document doesn't stop at the technicalities. It ventures into the societal implications and ethical considerations of diffusion models, ensuring a holistic understanding for readers.

In Conclusion

The field of visual computing is at an exciting juncture. As diffusion models continue to evolve and reshape the landscape, it's crucial to stay abreast of the latest developments. Whether you're a researcher, artist, or practitioner, understanding the nuances of diffusion models is indispensable. As visual computing and AI converge, the potential for innovation is boundless, promising a future where the creation and manipulation of visual content are limited only by our imagination. Source