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20240529【可控的视觉内容生成】陈铠：Geometric-Controllable Visual Generation: A Systematic ……
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报告嘉宾：陈铠 (香港科技大学) 报告时间：2024年5月29日 (星期三)晚上20:30 (北京时间) 报告题目：Geometric-Controllable Visual Generation: A Systematic Solution 报告人简介： Kai Chen is a PhD candidate in HKUST, supervised by Prof. Dit-Yan Yeung. His research aims at building generalizable AI systems via a data-centric perspective, especially in controllable generation for visual world modeling, Mixture-of-Experts (MoE) and (M)LLM self-alignment. He has published more than 10 papers in top conferences including CVPR, ICCV and ECCV, and actively served as academic reviewers and workshop organizers to promote development of the research community. 个人主页： https://kaichen1998.github.io/ 报告摘要： Controllability is an essential property to use generative models in real-life applications. Text prompts are currently considered as the primary conditions due to the superior interactivity with humans. However, different from language modeling, our visual world is 3D environment with precise geometric constraints. A typical case is that a robot can “turn left” in various ways, but the moving trajectory cannot be determined without the specific geometric information (e.g., angles and distances). In this talk, I will systematically discuss how to introduce geometric controls into foundational text-to-image generative models, which are then generalized to controllable video and 3D scene generation separately. Finally, I will discuss several remaining problems proposed in our ECCV 2024 W-CODA Workshop, which might finally lead us toward unified visual world modeling.
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