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Found 7 papers, 3 papers with code
A Controllable Co-Creative Agent for Game System Design
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.
Creativity as Variations on a Theme: Formalizations, Evidence, and Engineered Applications
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.
Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems
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.
Efficient 3D Object Reconstruction using Visual Transformers
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
Content-Based Search for Deep Generative Models
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
Creative Wand: A System to Study Effects of Communications in Co-Creative Settings
Recent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more.
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification
In this article, we estimate the edge-weights explicitly and use them for the downstream classification tasks - both semi-supervised and unsupervised.
