李明伟
Ph.D. Student @ Zhejiang University
I am a third-year Ph.D. student at Zhejiang University in Hangzhou, China, advised by Prof. Yi Yang. My research centers on generative visual intelligence, spanning controllable image and video generation, 3D vision, transparent-object geometry estimation, and digital humans. As part of my doctoral training, I am jointly trained with Beijing Zhongguancun Academy under the mentorship of Prof. Tie-Yan Liu. I am also a research intern at ByteDance, working on video generation and editing. Prior to that, I obtained my B.Sc. in Artificial Intelligence from Zhejiang University in 2023 (Outstanding Graduate, ranked 2nd in major). I was also a member of the Mixed Class at Chu Kochen Honors College (CKC) of Zhejiang University.
Multi-view stereo, Neural Radiance Fields (NeRF), and 3D Gaussian Splatting for high-quality scene reconstruction.
Surface normal estimation for transparent and reflective objects using foundation models.
Generative modeling, RGBA generation, visual understanding, and controllable content creation.
Ph.D. in Artificial Intelligence, College of Computer Science and Technology
Advisor: Prof. Yi Yang · Direct Ph.D. from Master's program
Joint training: Beijing Zhongguancun Academy · Mentor: Prof. Tie-Yan Liu
M.Sc. in Computer Science and Technology, College of Computer Science and Technology
Advisor: Prof. Yi Yang · Transferred to Ph.D. after one year
B.Sc. in Artificial Intelligence, College of Computer Science and Technology
Outstanding Graduate · Ranked 2nd in major · Recommended for graduate school
ICLR 2026
We propose a bidirectionally decoupled DPO method to resolve text-condition conflicts in controllable text-to-image generation, significantly improving both text and condition adherence.
ACM Multimedia 2025 Oral
We introduce normal and de-lighting diffusion priors to optimize transparent surface reconstruction, and design a sliding-window depth extraction method to improve geometric accuracy and rendering quality for transparent objects.
ICCV 2025
This work introduces text attribute hard-binding, image attribute hard-binding, and image attribute soft-binding mechanisms to enhance control precision in conditional text-to-image generation, achieving state-of-the-art on COCO-MIG benchmark.
Conference Reviewer: AAAI, ICML