Tuesday, November 22, 2022, 5pm

Deep generative models make visual content creation more accessible to novice and professional users alike by automating the synthesis of diverse, realistic content based on a collected dataset. People often use generative models as data-driven sources, making it challenging to personalize a model easily. Currently, personalizing a model requires careful data curation, which is too time-consuming and costly for everyday users. Hence, my research directs towards enabling billions of everyday users to easily collaborate, create, and share their personalized models without ML expertise.In my talk, I present two directions towards achieving this goal: (1) “human-in-the-loop model creation”: bypassing the bottleneck of data collection process, and enable users to directly create models using simple interfaces (e.g., sketches, control points), (2) “content-based model search”: building a search engine “Modelverse”, where neural-network-based models are indexed and searchable, allowing users to search and share personalized models, creating a whole new community of content creators.

Committee:
Jun-Yan Zhu
Shubham Tulsiani
Deepak Pathak
Yufei Ye

In Person and Zoom Participation. See announcement.

Event Type: Speaking Skills
Room Number: In Person and Virtual - ET
Building: Newell-Simon 4305 and Zoom
Speaker's Name: SHENG-YU WANG
Speaker Website: peterwang512.github.io
Speaker's Professional Title: Ph.D. Student, Robotics Institute, Carnegie Mellon University
Talk Title: Bridging Humans and Generative Models
For More Information: lyonsmuth@cmu.edu
Affiliations: Robotics Institute (RI)
Organization(s): SCS
Event Website Title: Event Website
Event Website URL: www.ri.cmu.edu…