Diffusion-based Controllable Image Generation: Methods, Applications and Future Directions

PhD Qualifying Examination


Title: "Diffusion-based Controllable Image Generation: Methods, Applications
and Future Directions"

by

Mr. Yanghao WANG


Abstract:

With the surge in image generation of recent years, the high-fidelity image 
generation problem has been well addressed by state-of-the-art generative 
models, i.e., diffusion models. However, humans' generation requirements have 
also exploded, which requires not only fidelity but also controllability, 
that is, controllable image generation. Specifically, controllable image 
generation leverages control signals to drive the generation process and aims 
to obtain regularized results that follow these signals. To better understand 
the context of controllable image generation, we focus on two aspects: 1) How 
to represent the control signals and inject them into the vanilla diffusion 
models. 2) How to unleash the capacity of controllable image generation for 
various applications. In this survey, we provide a comprehensive analysis of 
diffusion-based controllable image generation. First, we analyze its control 
methods and summarize them into nine mechanisms across three stages. 
Moreover, we demonstrate its abundant applications in seven scenarios. 
Finally, we discuss the existing challenges and the further extensions.


Date:                   Monday, 17 November 2025

Time:                   4:00pm - 6:00pm

Venue:                  Room 5566
                        Lifts 27/28

Committee Members:      Dr. Long Chen (Supervisor)
                        Prof. James Kwok (Chairperson)
                        Dr. Dan Xu