Details

Date

Location

Seminarraum FAV 01 B, Favoritenstraße 11, 1040 Wien

Visualization Design: from Heuristic to Generative

Speaker: Min Lu (Shenzhen University, China)

Abstract

Visualization transforms abstract data into visual forms to enhance human understanding. Its design has long relied on heuristic principles to create effective representations. In this talk, I will present our research journey, which ranges from principle-guided visual designs to generative AI-assisted methods. I will demonstrate how we leverage intelligent systems for automated layout, vectorization, and stylization, and will conclude with a new framework for AI-driven visualization design.

Bio

Min Lu is an Associate Professor at Shenzhen University, China, where she leads the Spatial Creative Intelligence and Interaction (SCII) Research Group. Her research focuses on AI-driven stylized design generation and interaction. She has published papers at top-tier conferences including SIGGRAPH, CVPR, IEEE VIS, and ACM CHI, and holds two grants from the National Natural Science Foundation of China (NSFC). Her work has been recognized with awards such as Best Paper Honorable Mentions at IEEE PacificVis 2024 and CVM 2020. She also serves on the program committees for ACM SIGGRAPH Asia, IEEE PacificVis, and ChinaVis. More information is available at: https://deardeer.github.io/.

Details

Duration

30 + 15
Category
Guest Talk
Host: Michael Wimmer

Attention and Semantic Control in Generative Models

Speaker: Daniel Cohen-Or (Tel Aviv University)

Abstract

Attention layers play a critical role in generative models. In this talk, I will show that these layers capture rich semantic information, and particularly semantic correspondences between elements within the image and across different images. Through several works, I will show that the rich representations learned by these layers can be leveraged for image manipulation, consistent image generation, and personalization. Additionally, I will discuss the challenges that arise, especially in scenarios involving complex prompts with multiple subjects. Specific issues, such as semantic leakage during the denoising process, can lead to inaccurate representations, resulting in poor generations.

Bio

Daniel Cohen-Or is a professor in the School of Computer Science. He received his B.Sc. cum laude in both mathematics and computer science (1985), and M.Sc. cum laude in computer science (1986) from Ben-Gurion University, and Ph.D. from the Department of Computer Science (1991) at State University of New York at Stony Brook. He was sitting on the editorial board of a number of international journals, and a member of the program committees of several international conferences. He was the recipient of the Eurographics Outstanding Technical Contributions Award in 2005. In 2013 he received The People’s Republic of China Friendship Award. In 2015 he was named a Thomson Reuters Highly Cited Researcher? He received the ACM SIGGRAPH Computer Graphics Achievement Award in 2018. In 2019 he won The Kadar Family Award for Outstanding Research. In 2020, he received The Eurographics Distinguished Career Award. His research interests are in computer graphics, in particular, synthesis, processing and modelling techniques.

Details

Duration

30 + 15
Category
Guest Talk
Host: Michael Wimmer