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:                   3:00pm - 5:00pm

Venue:                  Room 2128A
                        Lift 19

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