🚀 A New Era of Storytelling Without Limits.
To give creators the power to tell epic stories as they were meant to be told.
Guian Fang
I am a Ph.D. student at Show Lab, National University of Singapore, advised by Prof. Mike Zheng Shou.
Previously, I received my B.Eng. in HCPLab, Artificial Intelligence at the School of Intelligent Systems Engineering, Sun Yat-sen University, advised by Xiaodan Liang (梁小丹), co-supervised by Shengcai Liao.
Beyond research, I'm passionate about gaming and enjoy collecting various credit cards as a hobby. I believe in fostering open communication within the research community. Whether you'd like to chat about academic pursuits, share experiences, or explore collaborative opportunities, I'm always happy to connect.
Research & Projects
My research focuses on Generative Models for Vision, particularly Video World Models for video understanding and generation. Representative works are highlighted. * denotes equal contribution.
🎥 ACG Creation Made Easy for Everyone.
FramePrompt: In-context Controllable Animation with Zero Structural Changes
🌐 Generate 3D Worlds in Production with AI
Image-to-3D: Let AI do the heavy lifting so 3D Professionals can do the storytelling
📄 HumanRefiner:
Benchmarking Abnormal Human Generation and Refining with Coarse-to-fine Pose-Reversible Guidance
ECCV, 2024
A new large-scale benchmark (AbHuman) for anatomical anomalies in humans, and a plug-and-play method (HumanRefiner) for refining abnormal human generations with pose-reversible guidance.
📄 ChartThinker:
A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
LREC-Coling, 2024
ChartThinker leverages chain-of-thought reasoning and context retrieval to generate accurate and coherent chart summaries, outperforming previous methods on a diverse benchmark.
📄 LLaMA2-Accessory:
An Open-source Toolkit for LLM Development
An open-source toolkit supporting pretraining, finetuning, and deployment of large language and multimodal models, making LLM development more accessible and efficient.
📄 RealignDiff:
Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment
TNNLS, 2023
RealignDiff introduces a two-stage semantic re-alignment strategy to significantly improve the consistency between generated images and text prompts in diffusion models.
Honors & Awards
- 2nd Place, Asia and Pacific Mathematical Contest in Modeling (2022)
- Silver Medal, China Collegiate Algorithm Design & Programming Challenge Contest (2022)
- 2nd Place, Social Computing Innovation Competition (2022)
- 1st Place, National College Computer Ability Challenge (2023)
- Recipient, Huawei Intelligent Foundation Scholarship (2022)
- Recipient, National Encouragement Scholarship (2022)
- 1st Place, SYSU Outstanding Student Scholarship (2023)
- Recipient, National Scholarship (2023)
- Recipient, Li Xuerou Foundation Scholarship (2023)
Activities & Services
Conference Reviewer
- CV: ECCV, CVPR, ICCV
- ML: NeurIPS, ICLR, ICML
- NLP: ACL Rolling Review (ARR): ACL, EMNLP, NAACL, EACL
Program Committee
- AI: AAAI, AISTATS
Workshop Organizer
- LOVEU Workshop @ CVPR 2024: Long-form Video Understanding Towards Multimodal AI Assistant and Copilot
Teaching Assistant
- EE4309: Robot Perception
- EE5106: Advanced Robotics
Acknowledgements
I feel incredibly fortunate to have collaborated with such remarkable individuals who have generously offered me their mentorship.
