Join CMLab

• CMLab studies Generative Visual AI from Pixels to 4D Worlds.

• We develop AI technologies that can restore, generate, edit, and render visual data across images, videos, 3D scenes, and dynamic 4D worlds. Our research spans low-level vision, generative AI, 3D/4D visual computing, and real-world multimedia AI systems.

• We welcome highly self-motivated graduate and undergraduate students who want to conduct serious research in AI-based computer vision and multimedia.

• Our lab aims to publish at top-tier AI/CV venues and build visual AI systems with real-world impact.

Research Directions

• Please review our Research Area and Publications before reaching out, and mention specific topics you would like to work on.

Who We Are Looking For

• CMLab is best suited for students who:

• are strongly self-motivated and willing to learn independently
• enjoy coding, debugging, reading papers, and implementing research ideas
• want to publish papers at top-tier AI/CV conferences and journals
• are interested in long-term research rather than short-term experience only
• can actively communicate, discuss, and improve their research ideas

• Prior research experience is helpful, but not mandatory. However, strong learning attitude, coding ability, persistence, and intellectual curiosity are essential.

Graduate Applicants

• Prospective graduate students are encouraged to contact Prof. Jihyong Oh at least 3 to 6 months before applying.

• This allows both the applicant and the lab to evaluate research fit through paper reading, discussion, coding practice, and possible undergraduate research participation.

• Graduate students are expected to actively participate in research projects, read and discuss recent papers, implement ideas, write papers, and aim for top-tier AI/CV conferences and journals.

• Students in Ph.D. or integrated M.S./Ph.D. programs are strongly encouraged to present at international conferences and pursue internship opportunities at leading domestic or global AI companies.

Undergraduate Research Program

• The undergraduate research program is intended for students who are seriously considering graduate study in AI-based computer vision and multimedia.

• Selected undergraduate students may participate in paper reading, coding practice, research discussions, project-based mentoring, and implementation of recent AI/CV papers.

• Priority will be given to students who are seriously interested in joining CMLab as graduate students in the future.

• This program is not intended for short-term experience only. We prefer students who can participate consistently and prepare themselves for graduate-level research.

How to Contact

• If you are interested in joining CMLab, please send an email to: jihyongoh@cau.ac.kr

Email Subject

• [CMLab Application] {Graduate / Undergraduate} Research, Your Name, Current University

  • Example: [CMLab Application] Graduate Research, Gildong Hong, Chung-Ang University

Required Materials

• Please include the following materials in your email.

1. Full Curriculum Vitae (CV)

2. Academic transcript

3. Brief statement of research interests and future plan

4. Available participation period and expected weekly commitment

5. Optional: GitHub, project portfolio, paper implementation experience, writing sample, or personal website

• In your email, please briefly explain why you are interested in CMLab, which research topics you would like to explore, and what preparation you have already done.

• Applicants who demonstrate preparation, independent learning ability, and clear research motivation will be given higher priority.

Preparation

• Students are not expected to know everything before joining the lab. However, they should be willing to learn quickly and independently.

• Recommended preparation includes:

• Python and PyTorch
• Basic machine learning and deep learning
• Basic computer vision
• Linux, Git, and GitHub
• Paper reading and technical writing
• Depending on your interest: image/video enhancement/restoration, generative AI, diffusion models, 3DGS, NeRFs, or multimodal learning, etc.

• Independent learning habit is essential. Students are expected to search, read documentation, reproduce existing methods, debug code, and ask specific questions after making their own attempts.