no code implementations • 4 Aug 2023 • Rohan Agarwal, Zhiyu Lin, Mark Riedl
Many advancements have been made in procedural content generation for games, and with mixed-initiative co-creativity, have the potential for great benefits to human designers.
no code implementations • 7 May 2023 • Rohan Agarwal
There are many philosophies and theories on what creativity is and how it works, but one popular idea is that of variations on a theme and intersection of concepts.
1 code implementation • 3 May 2023 • Zhiyu Lin, Upol Ehsan, Rohan Agarwal, Samihan Dani, Vidushi Vashishth, Mark Riedl
We find out that MI-CC systems with more extensive coverage of the design space are rated higher or on par on a variety of creative and goal-completion metrics, demonstrating that wider coverage of the design space can improve user experience and achievement when using the system; Preference varies greatly between expertise groups, suggesting the development of adaptive, personalized MI-CC systems; Participants identified new design space dimensions including scrutability -- the ability to poke and prod at models -- and explainability.
no code implementations • 16 Feb 2023 • Rohan Agarwal, Wei Zhou, Xiaofeng Wu, Yuhan Li
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
1 code implementation • 6 Oct 2022 • Daohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, Mia Tang, David Bau, Jun-Yan Zhu
To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query.
Ranked #1 on Model Description Based Search on Generative Models
Contrastive Learning Image and Sketch based Model Retrieval +4
1 code implementation • 4 Aug 2022 • Zhiyu Lin, Rohan Agarwal, Mark Riedl
Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more.
no code implementations • 28 Feb 2022 • Rohan Agarwal, Aman Aziz, Aditya Suraj Krishnan, Aditya Challa, Sravan Danda
In this article, we estimate the edge-weights explicitly and use them for the downstream classification tasks - both semi-supervised and unsupervised.