Controllable Video Generation: Flexible and Fine-grained Generation

PhD Qualifying Examination


Title: "Controllable Video Generation: Flexible and Fine-grained Generation"

by

Mr. Yue MA


Abstract:

With the rapid development of AI-generated content (AIGC), video generation 
has emerged as one of its most dynamic and impactful subfields. In 
particular, the advancement of video generation foundation models has led to 
growing demand for controllable video generation methods that can more 
accurately reflect user intent. Most existing foundation models are designed 
for text-to-video generation, where text prompts alone are often insufficient 
to express complex, multi-modal, and fine-grained user requirements. This 
limitation makes it challenging for users to generate videos with precise 
control using current models. To address this issue, recent research has 
explored the integration of additional non-textual conditions, such as camera 
motion, depth maps, and human pose, to extend pretrained video generation 
models and enable more controllable video synthesis. These approaches aim to 
enhance the flexibility and practical applicability of AIGC-driven video 
generation systems. In this survey, we provide a systematic review of 
controllable video generation, covering both theoretical foundations and 
recent advances in the field. We begin by introducing the key concepts and 
commonly used open-source video generation models. We then focus on control 
mechanisms in video diffusion models, analyzing how different types of 
conditions can be incorporated into the denoising process to guide 
generation. Finally, we categorize existing methods based on the types of 
control signals they leverage, including single-condition generation, 
multi-condition generation, and universal controllable generation.


Date:                   Thursday, 11 December 2025

Time:                   4:00pm - 6:00pm

Venue:                  Room 2128A
                        Lift 19

Committee Members:      Dr. Qifeng Chen (Supervisor)
                        Dr. May Fung (Chairperson)
                        Dr. Dan Xu