Qifeng Chen

“He is the most exceptional undergraduate student I have seen over the past 17 years in the Hong Kong University of Science and Technology (HKUST),” professor Chi Keung Tang said highly of Qifeng Chen.

His research areas are diversified across multiple aspects of computer vision, including intrinsic image decomposition, stereo reconstruction, Markov Random Field (MRF) optimization, and optical flow estimation. Multiple papers were published on and selected for full oral presentations at ICCV and CVPR.

One of his creative innovations was applying MRF optimization to nonrigid registration of 3D surfaces for the first time. The resulting algorithm outperformed the state-of-the-art existing model and increased accuracy by 3 times. He basically overturned a decade of work on nonrigid registration and reset research in this area.

Most recently, Chen became an assistant professor at HKUST and moved to deep learning and image processing. He has been working on many innovative research topics, primarily focusing on how to revolutionize the image processing pipeline with deep learning techniques.

He is also working on an emerging research topic on photographic image synthesis from semantic layouts. For example, given a semantic layout of a scene, can an AI system synthesize an image that depicts the scene and output a photograph? Does AI have imagination? Can AI create animation autonomously?

"The answer is yes, as my research shows," Chen said confidently. He invented a semi-parametric approach based on Cascaded Refinement Networks to perform photographic image synthesis.

Chen is currently working on the development of an AI tool that aims to improve the authenticity of film visual contents and effects. This can reduce the cost of a movie by reducing the amount of manual work for creating such contents and effects. His goal is to upgrade this AI tool to automatically generate certain movie scenes or characters based on high-level descriptions such as written scripts or user scribbles.