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